path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
90122454/cell_33 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt()
data_train.Age.skew()
sns.displot(data_train.SibSp, kde=True) | code |
90122454/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt()
data_train.Age.skew() | code |
90122454/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
sns.displot(data_train.Pclass, kde=False) | code |
90122454/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Pclass.describe() | code |
90122454/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt()
data_train.Age.skew()
data_train.SibSp.describe() | code |
90122454/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt() | code |
90122454/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
sns.displot(data_train.Survived, kde=False) | code |
90122454/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
plt.figure(figsize=(24, 4))
sns.barplot(x=data_train.index[0:200], y=data_train.Survived[0:200]) | code |
90122454/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.kurt()
data_train.Age.skew()
data_train.Parch.describe() | code |
90122454/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Sex.describe() | code |
90122454/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Survived.describe() | code |
90122454/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train['Name'].value_counts() | code |
90122454/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.head() | code |
90122454/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.Age.describe() | code |
90122454/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_test = pd.read_csv(dirname + '/' + filenames[1])
data_train = pd.read_csv(dirname + '/' + filenames[0])
data_train.PassengerId.describe() | code |
90122454/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import norm, expon
from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, StandardScaler, minmax_scale
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score, train_test_split, cross_val_predict
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.linear_model import RidgeClassifierCV
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from keras.layers import Dense
from keras.models import Sequential
from keras.regularizers import l1
from lightgbm import LGBMClassifier
from optuna import create_study
from optuna.visualization import plot_optimization_history, plot_parallel_coordinate, plot_contour, plot_slice, plot_param_importances, plot_edf
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90137202/cell_42 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
labeled_barplot(df, 'int_memory', perc=True) | code |
90137202/cell_13 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes | code |
90137202/cell_9 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape | code |
90137202/cell_30 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
histogram_boxplot(df, 'days_used') | code |
90137202/cell_33 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
labeled_barplot(df, 'brand_name', perc=True) | code |
90137202/cell_44 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
labeled_barplot(df, 'selfie_camera_mp', perc=True) | code |
90137202/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
histogram_boxplot(df, 'used_price') | code |
90137202/cell_40 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
labeled_barplot(df, 'ram', perc=True) | code |
90137202/cell_39 | [
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
df['ram'].nunique() | code |
90137202/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
histogram_boxplot(df, 'weight') | code |
90137202/cell_48 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
df.groupby('brand_name')['ram'].mean()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
numeric_columns = df.select_dtypes(include=np.number).columns.tolist()
plt.figure(figsize=(15, 7))
sns.heatmap(df[numeric_columns].corr(), annot=True, vmin=-1, vmax=1, fmt='.2f', cmap='Spectral')
plt.show() | code |
90137202/cell_41 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
labeled_barplot(df, 'release_year', perc=True) | code |
90137202/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90137202/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.head() | code |
90137202/cell_51 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
df.groupby('brand_name')['ram'].mean()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
numeric_columns = df.select_dtypes(include=np.number).columns.tolist()
df.isnull().sum() | code |
90137202/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
histogram_boxplot(df, 'screen_size') | code |
90137202/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1) | code |
90137202/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum() | code |
90137202/cell_38 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
labeled_barplot(df, '5g', perc=True) | code |
90137202/cell_47 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
df.groupby('brand_name')['ram'].mean()
plt.figure(figsize=(15, 5))
plt.subplot(1, 2, 1)
sns.barplot(data=df, y='ram', x='brand_name')
plt.xticks(rotation=90)
plt.subplot(1, 2, 2)
sns.boxplot(data=df, y='ram', x='brand_name')
plt.xticks(rotation=90)
plt.show() | code |
90137202/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T | code |
90137202/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
labeled_barplot(df, 'os', perc=True) | code |
90137202/cell_43 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
labeled_barplot(df, 'main_camera_mp', perc=True) | code |
90137202/cell_46 | [
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
df.groupby('brand_name')['ram'].mean() | code |
90137202/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
histogram_boxplot(df, 'battery') | code |
90137202/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum() | code |
90137202/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
histogram_boxplot(df, 'new_price') | code |
90137202/cell_53 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
df.groupby('brand_name')['ram'].mean()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
numeric_columns = df.select_dtypes(include=np.number).columns.tolist()
df.isnull().sum()
df.isnull().sum() | code |
90137202/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
data.info() | code |
90137202/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import statsmodels.api as sm
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 200)
data = pd.read_csv('/kaggle/input/used-phone-data/used_phone_data.csv')
data.sample(n=5, random_state=1)
data.shape
df = data.copy()
category_col = df.select_dtypes(exclude=np.number).columns.tolist()
df[category_col] = df[category_col].astype('category')
df.dtypes
df.isnull().sum()
df.isnull().sum()
df.describe(include='all').T
# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (12,7))
kde: whether to the show density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a star will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to create labeled barplots for categorical and numerical variables
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 1, 5))
else:
plt.figure(figsize=(n + 1, 5))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n].sort_values(),
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
# show the plot
labeled_barplot(df, '4g', perc=True) | code |
73081309/cell_4 | [
"image_output_1.png"
] | import histomicstk as htk
import matplotlib.pyplot as plt
input_image_file = ('https://data.kitware.com/api/v1/file/'
'576ad39b8d777f1ecd6702f2/download') # Easy1.png
im_input = skimage.io.imread(input_image_file)[:, :, :3]
plt.imshow(im_input)
_ = plt.title('Input Image', fontsize=16)
ref_image_file = 'https://data.kitware.com/api/v1/file/57718cc28d777f1ecd8a883c/download'
im_reference = skimage.io.imread(ref_image_file)[:, :, :3]
mean_ref, std_ref = htk.preprocessing.color_conversion.lab_mean_std(im_reference)
im_nmzd = htk.preprocessing.color_normalization.reinhard(im_input, mean_ref, std_ref)
plt.figure(figsize=(20, 10))
plt.subplot(1, 2, 1)
plt.imshow(im_reference)
_ = plt.title('Reference Image', fontsize=titlesize)
plt.subplot(1, 2, 2)
plt.imshow(im_nmzd)
_ = plt.title('Normalized Input Image', fontsize=titlesize) | code |
73081309/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import histomicstk as htk
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
input_image_file = ('https://data.kitware.com/api/v1/file/'
'576ad39b8d777f1ecd6702f2/download') # Easy1.png
im_input = skimage.io.imread(input_image_file)[:, :, :3]
plt.imshow(im_input)
_ = plt.title('Input Image', fontsize=16)
# Load reference image for normalization
ref_image_file = ('https://data.kitware.com/api/v1/file/'
'57718cc28d777f1ecd8a883c/download') # L1.png
im_reference = skimage.io.imread(ref_image_file)[:, :, :3]
# get mean and stddev of reference image in lab space
mean_ref, std_ref = htk.preprocessing.color_conversion.lab_mean_std(im_reference)
# perform reinhard color normalization
im_nmzd = htk.preprocessing.color_normalization.reinhard(im_input, mean_ref, std_ref)
# Display results
plt.figure(figsize=(20, 10))
plt.subplot(1, 2, 1)
plt.imshow(im_reference)
_ = plt.title('Reference Image', fontsize=titlesize)
plt.subplot(1, 2, 2)
plt.imshow(im_nmzd)
_ = plt.title('Normalized Input Image', fontsize=titlesize)
# create stain to color map
stainColorMap = {
'hematoxylin': [0.65, 0.70, 0.29],
'eosin': [0.07, 0.99, 0.11],
'dab': [0.27, 0.57, 0.78],
'null': [0.0, 0.0, 0.0]
}
# specify stains of input image
stain_1 = 'hematoxylin' # nuclei stain
stain_2 = 'eosin' # cytoplasm stain
stain_3 = 'null' # set to null of input contains only two stains
# create stain matrix
W = np.array([stainColorMap[stain_1],
stainColorMap[stain_2],
stainColorMap[stain_3]]).T
# perform standard color deconvolution
im_stains = htk.preprocessing.color_deconvolution.color_deconvolution(im_nmzd, W).Stains
# Display results
plt.figure(figsize=(20, 10))
plt.subplot(1, 2, 1)
plt.imshow(im_stains[:, :, 0])
plt.title(stain_1, fontsize=titlesize)
plt.subplot(1, 2, 2)
plt.imshow(im_stains[:, :, 1])
_ = plt.title(stain_2, fontsize=titlesize)
im_nuclei_stain = im_stains[:, :, 0]
foreground_threshold = 60
im_fgnd_mask = sp.ndimage.morphology.binary_fill_holes(im_nuclei_stain < foreground_threshold)
min_radius = 10
max_radius = 15
im_log_max, im_sigma_max = htk.filters.shape.cdog(im_nuclei_stain, im_fgnd_mask, sigma_min=min_radius * np.sqrt(2), sigma_max=max_radius * np.sqrt(2))
local_max_search_radius = 10
im_nuclei_seg_mask, seeds, maxima = htk.segmentation.nuclear.max_clustering(im_log_max, im_fgnd_mask, local_max_search_radius)
min_nucleus_area = 80
im_nuclei_seg_mask = htk.segmentation.label.area_open(im_nuclei_seg_mask, min_nucleus_area).astype(np.int)
objProps = skimage.measure.regionprops(im_nuclei_seg_mask)
print('Number of nuclei = ', len(objProps))
plt.figure(figsize=(20, 10))
plt.subplot(1, 2, 1)
plt.imshow(skimage.color.label2rgb(im_nuclei_seg_mask, im_input, bg_label=0), origin='lower')
plt.title('Nuclei segmentation mask overlay', fontsize=titlesize)
plt.subplot(1, 2, 2)
plt.imshow(im_input)
plt.xlim([0, im_input.shape[1]])
plt.ylim([0, im_input.shape[0]])
plt.title('Nuclei bounding boxes', fontsize=titlesize)
for i in range(len(objProps)):
c = [objProps[i].centroid[1], objProps[i].centroid[0], 0]
width = objProps[i].bbox[3] - objProps[i].bbox[1] + 1
height = objProps[i].bbox[2] - objProps[i].bbox[0] + 1
cur_bbox = {'type': 'rectangle', 'center': c, 'width': width, 'height': height}
plt.plot(c[0], c[1], 'g+')
mrect = mpatches.Rectangle([c[0] - 0.5 * width, c[1] - 0.5 * height], width, height, fill=False, ec='g', linewidth=2)
plt.gca().add_patch(mrect) | code |
73081309/cell_1 | [
"text_plain_output_1.png"
] | !pip install histomicstk --find-links https://girder.github.io/large_image_wheels | code |
73081309/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
input_image_file = 'https://data.kitware.com/api/v1/file/576ad39b8d777f1ecd6702f2/download'
im_input = skimage.io.imread(input_image_file)[:, :, :3]
plt.imshow(im_input)
_ = plt.title('Input Image', fontsize=16) | code |
73081309/cell_5 | [
"image_output_1.png"
] | import histomicstk as htk
import matplotlib.pyplot as plt
import numpy as np
input_image_file = ('https://data.kitware.com/api/v1/file/'
'576ad39b8d777f1ecd6702f2/download') # Easy1.png
im_input = skimage.io.imread(input_image_file)[:, :, :3]
plt.imshow(im_input)
_ = plt.title('Input Image', fontsize=16)
# Load reference image for normalization
ref_image_file = ('https://data.kitware.com/api/v1/file/'
'57718cc28d777f1ecd8a883c/download') # L1.png
im_reference = skimage.io.imread(ref_image_file)[:, :, :3]
# get mean and stddev of reference image in lab space
mean_ref, std_ref = htk.preprocessing.color_conversion.lab_mean_std(im_reference)
# perform reinhard color normalization
im_nmzd = htk.preprocessing.color_normalization.reinhard(im_input, mean_ref, std_ref)
# Display results
plt.figure(figsize=(20, 10))
plt.subplot(1, 2, 1)
plt.imshow(im_reference)
_ = plt.title('Reference Image', fontsize=titlesize)
plt.subplot(1, 2, 2)
plt.imshow(im_nmzd)
_ = plt.title('Normalized Input Image', fontsize=titlesize)
stainColorMap = {'hematoxylin': [0.65, 0.7, 0.29], 'eosin': [0.07, 0.99, 0.11], 'dab': [0.27, 0.57, 0.78], 'null': [0.0, 0.0, 0.0]}
stain_1 = 'hematoxylin'
stain_2 = 'eosin'
stain_3 = 'null'
W = np.array([stainColorMap[stain_1], stainColorMap[stain_2], stainColorMap[stain_3]]).T
im_stains = htk.preprocessing.color_deconvolution.color_deconvolution(im_nmzd, W).Stains
plt.figure(figsize=(20, 10))
plt.subplot(1, 2, 1)
plt.imshow(im_stains[:, :, 0])
plt.title(stain_1, fontsize=titlesize)
plt.subplot(1, 2, 2)
plt.imshow(im_stains[:, :, 1])
_ = plt.title(stain_2, fontsize=titlesize) | code |
1005122/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/HR_comma_sep.csv')
data.head() | code |
72101196/cell_21 | [
"text_html_output_1.png"
] | from matplotlib.lines import Line2D
import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula
lin_reg = smf.ols(formula, data=df).fit()
lin_reg.summary()
err_series = lin_reg.params - lin_reg.conf_int()[0]
err_series
coef_df = pd.DataFrame({'coef': lin_reg.params.values[1:], 'err': err_series.values[1:], 'varname': err_series.index.values[1:]})
coef_df
formula_1 = 'fertility ~ %s' % ' + '.join(df.columns.values[1:-1])
mod_1 = smf.ols(formula_1, data=df).fit()
mod_1.params
formula_2 = 'fertility ~ %s' % ' + '.join(df.columns.values[1:-2].tolist() + ['infant_mort'])
mod_2 = smf.ols(formula_2, data=df).fit()
mod_2.params
coef_df = pd.DataFrame()
for i, mod in enumerate([mod_1, mod_2]):
err_series = mod.params - mod.conf_int()[0]
coef_df = coef_df.append(pd.DataFrame({'coef': mod.params.values[1:], 'err': err_series.values[1:], 'varname': err_series.index.values[1:], 'model': 'model %d' % (i + 1)}))
coef_df
marker_list = 'so'
width = 0.25
base_x = pd.np.arange(5) - 0.2
base_x
fig, ax = plt.subplots(figsize=(8, 5))
for i, mod in enumerate(coef_df.model.unique()):
mod_df = coef_df[coef_df.model == mod]
mod_df = mod_df.set_index('varname').reindex(coef_df['varname'].unique())
X = base_x + width * i
ax.bar(X, mod_df['coef'], color='none', yerr=mod_df['err'])
ax.set_ylabel('')
ax.set_xlabel('')
ax.scatter(x=X, marker=marker_list[i], s=120, y=mod_df['coef'], color='black')
ax.axhline(y=0, linestyle='--', color='black', linewidth=4)
ax.xaxis.set_ticks_position('none')
_ = ax.set_xticklabels(['', 'Agriculture', 'Exam', 'Edu.', 'Catholic', 'Infant Mort.'], rotation=0, fontsize=16)
fs = 16
ax.annotate('Control', xy=(0.3, -0.2), xytext=(0.3, -0.35), xycoords='axes fraction', textcoords='axes fraction', fontsize=fs, ha='center', va='bottom', bbox=dict(boxstyle='square', fc='white', ec='black'), arrowprops=dict(arrowstyle='-[, widthB=6.5, lengthB=1.2', lw=2.0, color='black'))
ax.annotate('Study', xy=(0.8, -0.2), xytext=(0.8, -0.35), xycoords='axes fraction', textcoords='axes fraction', fontsize=fs, ha='center', va='bottom', bbox=dict(boxstyle='square', fc='white', ec='black'), arrowprops=dict(arrowstyle='-[, widthB=3.5, lengthB=1.2', lw=2.0, color='black'))
legend_elements = [Line2D([0], [0], marker=m, label='Model %d' % i, color='k', markersize=10) for i, m in enumerate(marker_list)]
_ = ax.legend(handles=legend_elements, loc=2, prop={'size': 15}, labelspacing=1.2) | code |
72101196/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula
lin_reg = smf.ols(formula, data=df).fit()
lin_reg.summary()
err_series = lin_reg.params - lin_reg.conf_int()[0]
err_series
coef_df = pd.DataFrame({'coef': lin_reg.params.values[1:], 'err': err_series.values[1:], 'varname': err_series.index.values[1:]})
coef_df | code |
72101196/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula | code |
72101196/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula
lin_reg = smf.ols(formula, data=df).fit()
lin_reg.summary()
err_series = lin_reg.params - lin_reg.conf_int()[0]
err_series
coef_df = pd.DataFrame({'coef': lin_reg.params.values[1:], 'err': err_series.values[1:], 'varname': err_series.index.values[1:]})
coef_df
formula_1 = 'fertility ~ %s' % ' + '.join(df.columns.values[1:-1])
mod_1 = smf.ols(formula_1, data=df).fit()
mod_1.params
formula_2 = 'fertility ~ %s' % ' + '.join(df.columns.values[1:-2].tolist() + ['infant_mort'])
mod_2 = smf.ols(formula_2, data=df).fit()
mod_2.params
coef_df = pd.DataFrame()
for i, mod in enumerate([mod_1, mod_2]):
err_series = mod.params - mod.conf_int()[0]
coef_df = coef_df.append(pd.DataFrame({'coef': mod.params.values[1:], 'err': err_series.values[1:], 'varname': err_series.index.values[1:], 'model': 'model %d' % (i + 1)}))
coef_df
marker_list = 'so'
width = 0.25
base_x = pd.np.arange(5) - 0.2
base_x | code |
72101196/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.head() | code |
72101196/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula
lin_reg = smf.ols(formula, data=df).fit()
lin_reg.summary()
err_series = lin_reg.params - lin_reg.conf_int()[0]
err_series
coef_df = pd.DataFrame({'coef': lin_reg.params.values[1:], 'err': err_series.values[1:], 'varname': err_series.index.values[1:]})
coef_df
formula_1 = 'fertility ~ %s' % ' + '.join(df.columns.values[1:-1])
mod_1 = smf.ols(formula_1, data=df).fit()
mod_1.params
formula_2 = 'fertility ~ %s' % ' + '.join(df.columns.values[1:-2].tolist() + ['infant_mort'])
mod_2 = smf.ols(formula_2, data=df).fit()
mod_2.params
coef_df = pd.DataFrame()
for i, mod in enumerate([mod_1, mod_2]):
err_series = mod.params - mod.conf_int()[0]
coef_df = coef_df.append(pd.DataFrame({'coef': mod.params.values[1:], 'err': err_series.values[1:], 'varname': err_series.index.values[1:], 'model': 'model %d' % (i + 1)}))
coef_df | code |
72101196/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula
lin_reg = smf.ols(formula, data=df).fit()
lin_reg.summary()
formula_1 = 'fertility ~ %s' % ' + '.join(df.columns.values[1:-1])
print(formula_1)
mod_1 = smf.ols(formula_1, data=df).fit()
mod_1.params | code |
72101196/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula
lin_reg = smf.ols(formula, data=df).fit()
lin_reg.summary()
formula_1 = 'fertility ~ %s' % ' + '.join(df.columns.values[1:-1])
mod_1 = smf.ols(formula_1, data=df).fit()
mod_1.params
formula_2 = 'fertility ~ %s' % ' + '.join(df.columns.values[1:-2].tolist() + ['infant_mort'])
print(formula_2)
mod_2 = smf.ols(formula_2, data=df).fit()
mod_2.params | code |
72101196/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula
lin_reg = smf.ols(formula, data=df).fit()
lin_reg.summary() | code |
72101196/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.formula.api as smf
url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/swiss.csv'
df = pd.read_csv(url, index_col=0)
df.columns = ['fertility', 'agri', 'exam', 'edu', 'catholic', 'infant_mort']
formula = 'fertility ~ %s' % ' + '.join(df.columns.values[1:])
formula
lin_reg = smf.ols(formula, data=df).fit()
lin_reg.summary()
err_series = lin_reg.params - lin_reg.conf_int()[0]
err_series | code |
106209898/cell_4 | [
"text_plain_output_1.png"
] | def add(a, b):
pass
def add(a, b):
return a + b
addition = add(2, 10)
addition = addition + 10
print(addition) | code |
106209898/cell_6 | [
"text_plain_output_1.png"
] | def add(a, b):
pass
def add(a, b):
return a + b
def add(a, b):
add = a + b
sub = a - b
div = a / b
mul = a * b
return (add, sub, div, mul)
a, b, c, d = add(2, 10)
print(a, b, c, d) | code |
106209898/cell_2 | [
"text_plain_output_1.png"
] | def add(a, b):
pass
add(2, 10) | code |
2001740/cell_4 | [
"text_plain_output_1.png"
] | from datetime import datetime
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_store_info.csv')
pd_air_visit_data = pd.read_csv('../input/air_visit_data.csv')
pd_date_info = pd.read_csv('../input/date_info.csv')
pd_hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
pd_hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
pd_store_id_relation = pd.read_csv('../input/store_id_relation.csv')
pd_sample_submission = pd.read_csv('../input/sample_submission.csv')
from datetime import datetime
pd_air_reserve['new_visit_date'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').date() for d in pd_air_reserve['visit_datetime']]
pd_air_reserve['new_visit_time'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').time() for d in pd_air_reserve['visit_datetime']]
pd_air_reserve['new_reserve_date'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').date() for d in pd_air_reserve['reserve_datetime']]
pd_air_reserve['new_reserve_time'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').time() for d in pd_air_reserve['reserve_datetime']]
pd_air_reserve.groupby(['air_store_id']['new_visit_date']).agg({'reserve_visitors': sum, 'new_visit_time': [min, max, mean]})
pd_air_reserve_summ = pd_air_reserve.groupby(['air_store_id', 'new_visit_date']).agg({'reserve_visitors': sum, 'new_visit_time': [min, max]})
print(pd_air_reserve_summ.describe(include='all').transpose())
pd_air_reserve_summ.head()
print(pd_air_reserve_summ.query('air_store_id=="air_00a91d42b08b08d9"')) | code |
2001740/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import plotly.plotly as py
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_store_info.csv')
pd_air_visit_data = pd.read_csv('../input/air_visit_data.csv')
pd_date_info = pd.read_csv('../input/date_info.csv')
pd_hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
pd_hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
pd_store_id_relation = pd.read_csv('../input/store_id_relation.csv')
pd_sample_submission = pd.read_csv('../input/sample_submission.csv')
from datetime import datetime
pd_air_reserve['new_visit_date'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').date() for d in pd_air_reserve['visit_datetime']]
pd_air_reserve['new_visit_time'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').time() for d in pd_air_reserve['visit_datetime']]
pd_air_reserve['new_reserve_date'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').date() for d in pd_air_reserve['reserve_datetime']]
pd_air_reserve['new_reserve_time'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').time() for d in pd_air_reserve['reserve_datetime']]
pd_air_reserve.groupby(['air_store_id']['new_visit_date']).agg({'reserve_visitors': sum, 'new_visit_time': [min, max, mean]})
pd_air_reserve_summ = pd_air_reserve.groupby(['air_store_id', 'new_visit_date']).agg({'reserve_visitors': sum, 'new_visit_time': [min, max]})
temp = pd_air_reserve_summ.query('air_store_id=="air_00a91d42b08b08d9"')
x = temp['new_visit_date']
import plotly.plotly as py
import plotly.graph_objs as go
pd_air_reserve_summ.index
x = pd_air_reserve_summ['air_00a91d42b08b08d9']
y = pd_air_reserve_summ['air_store_id' == 'air_00a91d42b08b08d9', 'reserve_visitors', 'sum']
trace0 = go.Scatter(x, y, name='High 2014', line=dict(color='rgb(205, 12, 24)', width=4))
data = [trace0]
layout = dict(title='Average High and Low Temperatures in New York', xaxis=dict(title='Month'), yaxis=dict(title='Temperature (degrees F)'))
fig = dict(data=data, layout=layout)
py.iplot(fig, filename='styled-line') | code |
2001740/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_store_info.csv')
pd_air_visit_data = pd.read_csv('../input/air_visit_data.csv')
pd_date_info = pd.read_csv('../input/date_info.csv')
pd_hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
pd_hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
pd_store_id_relation = pd.read_csv('../input/store_id_relation.csv')
pd_sample_submission = pd.read_csv('../input/sample_submission.csv')
print(pd_hpg_reserve.head())
print(pd_hpg_store_info.head())
print(pd_store_id_relation.head())
print(pd_sample_submission.head())
pd_air_reserve.describe().transpose() | code |
2001740/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_store_info.csv')
pd_air_visit_data = pd.read_csv('../input/air_visit_data.csv')
pd_date_info = pd.read_csv('../input/date_info.csv')
print(pd_air_reserve.tail())
print(pd_air_store_info.tail())
print(pd_air_visit_data.tail())
print(pd_date_info.head()) | code |
2001740/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from datetime import datetime
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_store_info.csv')
pd_air_visit_data = pd.read_csv('../input/air_visit_data.csv')
pd_date_info = pd.read_csv('../input/date_info.csv')
pd_hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
pd_hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
pd_store_id_relation = pd.read_csv('../input/store_id_relation.csv')
pd_sample_submission = pd.read_csv('../input/sample_submission.csv')
print(pd_air_reserve.describe(include='all').transpose())
from datetime import datetime
pd_air_reserve['new_visit_date'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').date() for d in pd_air_reserve['visit_datetime']]
pd_air_reserve['new_visit_time'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').time() for d in pd_air_reserve['visit_datetime']]
pd_air_reserve['new_reserve_date'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').date() for d in pd_air_reserve['reserve_datetime']]
pd_air_reserve['new_reserve_time'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').time() for d in pd_air_reserve['reserve_datetime']]
print(pd_air_reserve.describe(include='all').transpose())
pd_air_reserve.groupby(['air_store_id']['new_visit_date']).agg({'reserve_visitors': sum, 'new_visit_time': [min, max, mean]}) | code |
2001740/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
pd_air_reserve = pd.read_csv('../input/air_reserve.csv')
pd_air_store_info = pd.read_csv('../input/air_store_info.csv')
pd_air_visit_data = pd.read_csv('../input/air_visit_data.csv')
pd_date_info = pd.read_csv('../input/date_info.csv')
pd_hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
pd_hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
pd_store_id_relation = pd.read_csv('../input/store_id_relation.csv')
pd_sample_submission = pd.read_csv('../input/sample_submission.csv')
from datetime import datetime
pd_air_reserve['new_visit_date'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').date() for d in pd_air_reserve['visit_datetime']]
pd_air_reserve['new_visit_time'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').time() for d in pd_air_reserve['visit_datetime']]
pd_air_reserve['new_reserve_date'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').date() for d in pd_air_reserve['reserve_datetime']]
pd_air_reserve['new_reserve_time'] = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S').time() for d in pd_air_reserve['reserve_datetime']]
pd_air_reserve.groupby(['air_store_id']['new_visit_date']).agg({'reserve_visitors': sum, 'new_visit_time': [min, max, mean]})
pd_air_reserve_summ = pd_air_reserve.groupby(['air_store_id', 'new_visit_date']).agg({'reserve_visitors': sum, 'new_visit_time': [min, max]})
temp = pd_air_reserve_summ.query('air_store_id=="air_00a91d42b08b08d9"')
x = temp['new_visit_date'] | code |
1006177/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names() | code |
1006177/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']]
data_y = data_train['Survived']
age_mean = data_x['Age'].dropna().median()
fare_mean = data_x['Fare'].dropna().median()
data_x['Age'].fillna(age_mean, inplace=True)
data_x['Fare'].fillna(fare_mean, inplace=True)
data_x['Embarked'].fillna('S', inplace=True)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts() | code |
1006177/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']]
data_y = data_train['Survived']
age_mean = data_x['Age'].dropna().median()
fare_mean = data_x['Fare'].dropna().median()
data_x['Age'].fillna(age_mean, inplace=True)
data_x['Fare'].fillna(fare_mean, inplace=True)
data_x['Embarked'].fillna('S', inplace=True)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts()
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names()
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(x_train, y_train)
dtc_y_predict = dtc.predict(x_test)
dtc.score(x_test, y_test)
run_x = data_test[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']]
run_x['Age'].fillna(age_mean, inplace=True)
run_x['Fare'].fillna(fare_mean, inplace=True)
for i in range(1, 4):
run_x.loc[run_x.Pclass == i, 'Pclass'] = str(i)
run_x = vec.transform(run_x.to_dict(orient='record'))
run_y_predict = dtc.predict(run_x) | code |
1006177/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']]
data_y = data_train['Survived']
age_mean = data_x['Age'].dropna().median()
fare_mean = data_x['Fare'].dropna().median()
data_x['Age'].fillna(age_mean, inplace=True)
data_x['Fare'].fillna(fare_mean, inplace=True)
data_x['Embarked'].fillna('S', inplace=True)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts()
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names()
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(x_train, y_train)
dtc_y_predict = dtc.predict(x_test)
dtc.score(x_test, y_test) | code |
1006177/cell_16 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']]
data_y = data_train['Survived']
age_mean = data_x['Age'].dropna().median()
fare_mean = data_x['Fare'].dropna().median()
data_x['Age'].fillna(age_mean, inplace=True)
data_x['Fare'].fillna(fare_mean, inplace=True)
data_x['Embarked'].fillna('S', inplace=True)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts()
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names()
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(x_train, y_train)
dtc_y_predict = dtc.predict(x_test)
print(classification_report(y_test, dtc_y_predict, target_names=['died', 'surived'])) | code |
1006177/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_train.info() | code |
1006177/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']]
data_y = data_train['Survived']
age_mean = data_x['Age'].dropna().median()
fare_mean = data_x['Fare'].dropna().median()
data_x['Age'].fillna(age_mean, inplace=True)
data_x['Fare'].fillna(fare_mean, inplace=True)
data_x['Embarked'].fillna('S', inplace=True)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
data_x.head() | code |
17101114/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain) | code |
17101114/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
joblib_file = 'joblib_RL_Model.pkl'
joblib.dump(LR_Model, joblib_file) | code |
17101114/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.externals import joblib | code |
17101114/cell_29 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
import pickle
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
Pkl_Filename = 'Pickle_RL_Model.pkl'
with open(Pkl_Filename, 'wb') as file:
pickle.dump(LR_Model, file)
with open(Pkl_Filename, 'rb') as file:
Pickled_LR_Model = pickle.load(file)
Pickled_LR_Model
score = Pickled_LR_Model.score(Xtest, Ytest)
Ypredict = Pickled_LR_Model.predict(Xtest)
Ypredict
joblib_file = 'joblib_RL_Model.pkl'
joblib.dump(LR_Model, joblib_file)
joblib_LR_model = joblib.load(joblib_file)
joblib_LR_model
score = joblib_LR_model.score(Xtest, Ytest)
print('Test score: {0:.2f} %'.format(100 * score))
Ypredict = joblib_LR_model.predict(Xtest)
Ypredict | code |
17101114/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pickle
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
Pkl_Filename = 'Pickle_RL_Model.pkl'
with open(Pkl_Filename, 'wb') as file:
pickle.dump(LR_Model, file)
with open(Pkl_Filename, 'rb') as file:
Pickled_LR_Model = pickle.load(file)
Pickled_LR_Model
score = Pickled_LR_Model.score(Xtest, Ytest)
print('Test score: {0:.2f} %'.format(100 * score))
Ypredict = Pickled_LR_Model.predict(Xtest)
Ypredict | code |
17101114/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pickle
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
Pkl_Filename = 'Pickle_RL_Model.pkl'
with open(Pkl_Filename, 'wb') as file:
pickle.dump(LR_Model, file)
with open(Pkl_Filename, 'rb') as file:
Pickled_LR_Model = pickle.load(file)
Pickled_LR_Model | code |
17101114/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
LR_Model = LogisticRegression(C=0.1, max_iter=20, fit_intercept=True, n_jobs=3, solver='liblinear')
LR_Model.fit(Xtrain, Ytrain)
joblib_file = 'joblib_RL_Model.pkl'
joblib.dump(LR_Model, joblib_file)
joblib_LR_model = joblib.load(joblib_file)
joblib_LR_model | code |
129022282/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
df_train['Status'].value_counts() | code |
129022282/cell_25 | [
"text_html_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
smt = SMOTE(random_state=0)
xtrain_res, ytrain_res = smt.fit_resample(xtrain, ytrain)
rf = RandomForestClassifier(max_depth=3, random_state=0)
rf.fit(xtrain_res, ytrain_res) | code |
129022282/cell_23 | [
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
smt = SMOTE(random_state=0)
xtrain_res, ytrain_res = smt.fit_resample(xtrain, ytrain)
print(f'Distribuition BEFORE balancing:\n{ytrain.value_counts()}')
print()
print(f'Distribuition AFTER balancing:\n{ytrain_res.value_counts()}') | code |
129022282/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_test.head() | code |
129022282/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
smt = SMOTE(random_state=0)
xtrain_res, ytrain_res = smt.fit_resample(xtrain, ytrain)
rf = RandomForestClassifier(max_depth=3, random_state=0)
rf.fit(xtrain_res, ytrain_res)
ypred = rf.predict(xtest)
print('Cccuracy:', accuracy_score(ytest, ypred)) | code |
129022282/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
columns_list = list(df_train.columns)
for i in range(len(columns_list)):
plt.xticks(rotation=45)
plt.tight_layout()
le = LabelEncoder()
str_col = df_train.select_dtypes(include='object').columns
for c in str_col:
df_train[c] = le.fit_transform(df_train[c])
df_train.head() | code |
129022282/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.info() | code |
129022282/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
columns_list = list(df_train.columns)
for i in range(len(columns_list)):
plt.xticks(rotation=45)
plt.tight_layout()
le = LabelEncoder()
str_col = df_train.select_dtypes(include='object').columns
for c in str_col:
df_train[c] = le.fit_transform(df_train[c])
plt.figure(figsize=(16, 10))
sns.heatmap(df_train.corr(), annot=True) | code |
129022282/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum() | code |
129022282/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
columns_list = list(df_train.columns)
for i in range(len(columns_list)):
plt.xticks(rotation=45)
plt.tight_layout()
le = LabelEncoder()
str_col = df_train.select_dtypes(include='object').columns
for c in str_col:
df_train[c] = le.fit_transform(df_train[c])
df_train.head() | code |
129022282/cell_31 | [
"text_html_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
columns_list = list(df_train.columns)
for i in range(len(columns_list)):
plt.xticks(rotation=45)
plt.tight_layout()
le = LabelEncoder()
str_col = df_train.select_dtypes(include='object').columns
for c in str_col:
df_train[c] = le.fit_transform(df_train[c])
smt = SMOTE(random_state=0)
xtrain_res, ytrain_res = smt.fit_resample(xtrain, ytrain)
rf = RandomForestClassifier(max_depth=3, random_state=0)
rf.fit(xtrain_res, ytrain_res)
ypred = rf.predict(xtest)
le = LabelEncoder()
str_col = df_test.select_dtypes(include='object').columns
for c in str_col:
df_test[c] = le.fit_transform(df_test[c])
test_predict = rf.predict(df_test)
test_result = pd.DataFrame(df_test)
test_result['predicted_status'] = test_predict
test_result.head() | code |
129022282/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum()
columns_list = list(df_train.columns)
plt.figure(figsize=(12, 20))
for i in range(len(columns_list)):
plt.subplot(5, 3, i + 1)
plt.title(columns_list[i])
plt.xticks(rotation=45)
plt.hist(df_train[columns_list[i]])
plt.tight_layout() | code |
129022282/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.isna().sum()
df_train.isna().sum() | code |
129022282/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/loan-data-set/loan_train.csv')
df_test = pd.read_csv('/kaggle/input/loan-data-set/loan_test.csv')
df_train.head() | code |
72106185/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
train
train.drop('id', axis=1, inplace=True)
na_count = pd.Series([train[col].isna().sum() for col in train.columns], index=train.columns, name='NA Count')
na_count
y = train['target']
X = train.drop('target', axis=1)
num_cols = [col for col in X.columns if X[col].dtype in ['int64', 'float64']]
cat_cols = [col for col in X.columns if X[col].dtype == 'object' and X[col].nunique() < 10]
cardinality = pd.Series([X[col].nunique() for col in cat_cols], index=cat_cols, name='Cardinality')
cardinality
from sklearn.preprocessing import OneHotEncoder
OHE = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OHE.fit_transform(X_train[cat_cols]))
OH_cols_valid = pd.DataFrame(OHE.transform(X_valid[cat_cols]))
OH_cols_train.index = X_train.index
OH_cols_valid.index = X_valid.index
num_X_train = X_train.drop(cat_cols, axis=1)
num_X_valid = X_valid.drop(cat_cols, axis=1)
OH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
model = XGBRegressor(n_estimators=600, n_jobs=4, learning_rate=0.05, random_state=0)
model.fit(OH_X_train, y_train)
preds = model.predict(OH_X_valid)
print('RMSE: ', mean_squared_error(preds, y_valid, squared=False)) | code |