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Basic_pandas_1

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using some of the basic pandas functions_and usefull for the very beginig when started practice on the datasets using python laibraries

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1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "id": "9afe08a5",
7
+ "metadata": {},
8
+ "outputs": [],
9
+ "source": [
10
+ "import pandas as pd"
11
+ ]
12
+ },
13
+ {
14
+ "cell_type": "code",
15
+ "execution_count": 2,
16
+ "id": "c5856fa5",
17
+ "metadata": {},
18
+ "outputs": [
19
+ {
20
+ "ename": "TypeError",
21
+ "evalue": "Index(...) must be called with a collection of some kind, 2 was passed",
22
+ "output_type": "error",
23
+ "traceback": [
24
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
25
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
26
+ "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df\u001b[38;5;241m=\u001b[39m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n",
27
+ "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/series.py:380\u001b[0m, in \u001b[0;36mSeries.__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[38;5;66;03m# uncomment the line below when removing the FutureWarning\u001b[39;00m\n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# dtype = np.dtype(object)\u001b[39;00m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 380\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43mensure_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 382\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 383\u001b[0m data \u001b[38;5;241m=\u001b[39m {}\n",
28
+ "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:7043\u001b[0m, in \u001b[0;36mensure_index\u001b[0;34m(index_like, copy)\u001b[0m\n\u001b[1;32m 7041\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index\u001b[38;5;241m.\u001b[39m_with_infer(index_like, copy\u001b[38;5;241m=\u001b[39mcopy, tupleize_cols\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 7042\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 7043\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mIndex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_with_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
29
+ "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:680\u001b[0m, in \u001b[0;36mIndex._with_infer\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 678\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 679\u001b[0m warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.*the Index constructor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mFutureWarning\u001b[39;00m)\n\u001b[0;32m--> 680\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 682\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m _dtype_obj \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m result\u001b[38;5;241m.\u001b[39m_is_multi:\n\u001b[1;32m 683\u001b[0m \u001b[38;5;66;03m# error: Argument 1 to \"maybe_convert_objects\" has incompatible type\u001b[39;00m\n\u001b[1;32m 684\u001b[0m \u001b[38;5;66;03m# \"Union[ExtensionArray, ndarray[Any, Any]]\"; expected\u001b[39;00m\n\u001b[1;32m 685\u001b[0m \u001b[38;5;66;03m# \"ndarray[Any, Any]\"\u001b[39;00m\n\u001b[1;32m 686\u001b[0m values \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mmaybe_convert_objects(result\u001b[38;5;241m.\u001b[39m_values) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n",
30
+ "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:508\u001b[0m, in \u001b[0;36mIndex.__new__\u001b[0;34m(cls, data, dtype, copy, name, tupleize_cols, **kwargs)\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m klass\u001b[38;5;241m.\u001b[39m_simple_new(arr, name)\n\u001b[1;32m 507\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_scalar(data):\n\u001b[0;32m--> 508\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_scalar_data_error(data)\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(data, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__array__\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 510\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index(np\u001b[38;5;241m.\u001b[39masarray(data), dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, name\u001b[38;5;241m=\u001b[39mname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
31
+ "\u001b[0;31mTypeError\u001b[0m: Index(...) must be called with a collection of some kind, 2 was passed"
32
+ ]
33
+ }
34
+ ],
35
+ "source": [
36
+ "df=pd.Series(1,2,3,4,5)\n",
37
+ "print(df)"
38
+ ]
39
+ },
40
+ {
41
+ "cell_type": "code",
42
+ "execution_count": 1,
43
+ "id": "422aaa0b",
44
+ "metadata": {},
45
+ "outputs": [
46
+ {
47
+ "ename": "NameError",
48
+ "evalue": "name 'pd' is not defined",
49
+ "output_type": "error",
50
+ "traceback": [
51
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
52
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
53
+ "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dataset\u001b[38;5;241m=\u001b[39m\u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAdvertising.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
54
+ "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
55
+ ]
56
+ }
57
+ ],
58
+ "source": [
59
+ "dataset=pd.read_csv('Advertising.csv')"
60
+ ]
61
+ },
62
+ {
63
+ "cell_type": "code",
64
+ "execution_count": 2,
65
+ "id": "6b4ba291",
66
+ "metadata": {},
67
+ "outputs": [],
68
+ "source": [
69
+ "import pandas as pd\n",
70
+ "dataset=pd.read_csv('Advertising.csv')"
71
+ ]
72
+ },
73
+ {
74
+ "cell_type": "code",
75
+ "execution_count": 3,
76
+ "id": "2ec925f7",
77
+ "metadata": {},
78
+ "outputs": [
79
+ {
80
+ "name": "stdout",
81
+ "output_type": "stream",
82
+ "text": [
83
+ " Unnamed: 0 TV radio newspaper sales\n",
84
+ "0 1 230.1 37.8 69.2 22.1\n",
85
+ "1 2 44.5 39.3 45.1 10.4\n",
86
+ "2 3 17.2 45.9 69.3 9.3\n",
87
+ "3 4 151.5 41.3 58.5 18.5\n",
88
+ "4 5 180.8 10.8 58.4 12.9\n"
89
+ ]
90
+ }
91
+ ],
92
+ "source": [
93
+ "print(dataset.head())"
94
+ ]
95
+ },
96
+ {
97
+ "cell_type": "code",
98
+ "execution_count": 4,
99
+ "id": "4d33fa27",
100
+ "metadata": {},
101
+ "outputs": [
102
+ {
103
+ "name": "stdout",
104
+ "output_type": "stream",
105
+ "text": [
106
+ "<class 'pandas.core.frame.DataFrame'>\n",
107
+ "RangeIndex: 200 entries, 0 to 199\n",
108
+ "Data columns (total 5 columns):\n",
109
+ " # Column Non-Null Count Dtype \n",
110
+ "--- ------ -------------- ----- \n",
111
+ " 0 Unnamed: 0 200 non-null int64 \n",
112
+ " 1 TV 200 non-null float64\n",
113
+ " 2 radio 200 non-null float64\n",
114
+ " 3 newspaper 200 non-null float64\n",
115
+ " 4 sales 200 non-null float64\n",
116
+ "dtypes: float64(4), int64(1)\n",
117
+ "memory usage: 7.9 KB\n",
118
+ "None\n"
119
+ ]
120
+ }
121
+ ],
122
+ "source": [
123
+ "print(dataset.info())"
124
+ ]
125
+ },
126
+ {
127
+ "cell_type": "code",
128
+ "execution_count": 6,
129
+ "id": "d9d1a560",
130
+ "metadata": {},
131
+ "outputs": [
132
+ {
133
+ "name": "stdout",
134
+ "output_type": "stream",
135
+ "text": [
136
+ " animal_name hair feathers eggs milk airborne aquatic predator \\\n",
137
+ "0 aardvark 1 0 0 1 0 0 1 \n",
138
+ "1 antelope 1 0 0 1 0 0 0 \n",
139
+ "2 bass 0 0 1 0 0 1 1 \n",
140
+ "3 bear 1 0 0 1 0 0 1 \n",
141
+ "4 boar 1 0 0 1 0 0 1 \n",
142
+ "\n",
143
+ " toothed backbone breathes venomous fins legs tail domestic catsize \\\n",
144
+ "0 1 1 1 0 0 4 0 0 1 \n",
145
+ "1 1 1 1 0 0 4 1 0 1 \n",
146
+ "2 1 1 0 0 1 0 1 0 0 \n",
147
+ "3 1 1 1 0 0 4 0 0 1 \n",
148
+ "4 1 1 1 0 0 4 1 0 1 \n",
149
+ "\n",
150
+ " class_type \n",
151
+ "0 1 \n",
152
+ "1 1 \n",
153
+ "2 4 \n",
154
+ "3 1 \n",
155
+ "4 1 \n"
156
+ ]
157
+ }
158
+ ],
159
+ "source": [
160
+ "dataset2=pd.read_csv('zoo.csv')\n",
161
+ "print(dataset2.head())"
162
+ ]
163
+ },
164
+ {
165
+ "cell_type": "code",
166
+ "execution_count": 7,
167
+ "id": "9a48dde4",
168
+ "metadata": {},
169
+ "outputs": [
170
+ {
171
+ "name": "stdout",
172
+ "output_type": "stream",
173
+ "text": [
174
+ "<class 'pandas.core.frame.DataFrame'>\n",
175
+ "RangeIndex: 101 entries, 0 to 100\n",
176
+ "Data columns (total 18 columns):\n",
177
+ " # Column Non-Null Count Dtype \n",
178
+ "--- ------ -------------- ----- \n",
179
+ " 0 animal_name 101 non-null object\n",
180
+ " 1 hair 101 non-null int64 \n",
181
+ " 2 feathers 101 non-null int64 \n",
182
+ " 3 eggs 101 non-null int64 \n",
183
+ " 4 milk 101 non-null int64 \n",
184
+ " 5 airborne 101 non-null int64 \n",
185
+ " 6 aquatic 101 non-null int64 \n",
186
+ " 7 predator 101 non-null int64 \n",
187
+ " 8 toothed 101 non-null int64 \n",
188
+ " 9 backbone 101 non-null int64 \n",
189
+ " 10 breathes 101 non-null int64 \n",
190
+ " 11 venomous 101 non-null int64 \n",
191
+ " 12 fins 101 non-null int64 \n",
192
+ " 13 legs 101 non-null int64 \n",
193
+ " 14 tail 101 non-null int64 \n",
194
+ " 15 domestic 101 non-null int64 \n",
195
+ " 16 catsize 101 non-null int64 \n",
196
+ " 17 class_type 101 non-null int64 \n",
197
+ "dtypes: int64(17), object(1)\n",
198
+ "memory usage: 14.3+ KB\n",
199
+ "None\n"
200
+ ]
201
+ }
202
+ ],
203
+ "source": [
204
+ "print(dataset2.info())"
205
+ ]
206
+ },
207
+ {
208
+ "cell_type": "code",
209
+ "execution_count": 8,
210
+ "id": "a2ba3d6c",
211
+ "metadata": {},
212
+ "outputs": [
213
+ {
214
+ "name": "stdout",
215
+ "output_type": "stream",
216
+ "text": [
217
+ " Unnamed: 0 TV radio newspaper sales\n",
218
+ "0 1 230.1 37.8 69.2 22.1\n",
219
+ "1 2 44.5 39.3 45.1 10.4\n",
220
+ "2 3 17.2 45.9 69.3 9.3\n",
221
+ "3 4 151.5 41.3 58.5 18.5\n",
222
+ "4 5 180.8 10.8 58.4 12.9\n",
223
+ "5 6 8.7 48.9 75.0 7.2\n",
224
+ "6 7 57.5 32.8 23.5 11.8\n",
225
+ "7 8 120.2 19.6 11.6 13.2\n",
226
+ "8 9 8.6 2.1 1.0 4.8\n",
227
+ "9 10 199.8 2.6 21.2 10.6\n",
228
+ "10 11 66.1 5.8 24.2 8.6\n",
229
+ "11 12 214.7 24.0 4.0 17.4\n",
230
+ "12 13 23.8 35.1 65.9 9.2\n",
231
+ "13 14 97.5 7.6 7.2 9.7\n",
232
+ "14 15 204.1 32.9 46.0 19.0\n",
233
+ "15 16 195.4 47.7 52.9 22.4\n",
234
+ "16 17 67.8 36.6 114.0 12.5\n",
235
+ "17 18 281.4 39.6 55.8 24.4\n",
236
+ "18 19 69.2 20.5 18.3 11.3\n",
237
+ "19 20 147.3 23.9 19.1 14.6\n",
238
+ "20 21 218.4 27.7 53.4 18.0\n",
239
+ "21 22 237.4 5.1 23.5 12.5\n",
240
+ "22 23 13.2 15.9 49.6 5.6\n",
241
+ "23 24 228.3 16.9 26.2 15.5\n",
242
+ "24 25 62.3 12.6 18.3 9.7\n",
243
+ "25 26 262.9 3.5 19.5 12.0\n",
244
+ "26 27 142.9 29.3 12.6 15.0\n",
245
+ "27 28 240.1 16.7 22.9 15.9\n",
246
+ "28 29 248.8 27.1 22.9 18.9\n",
247
+ "29 30 70.6 16.0 40.8 10.5\n",
248
+ "30 31 292.9 28.3 43.2 21.4\n",
249
+ "31 32 112.9 17.4 38.6 11.9\n",
250
+ "32 33 97.2 1.5 30.0 9.6\n",
251
+ "33 34 265.6 20.0 0.3 17.4\n",
252
+ "34 35 95.7 1.4 7.4 9.5\n",
253
+ "35 36 290.7 4.1 8.5 12.8\n",
254
+ "36 37 266.9 43.8 5.0 25.4\n",
255
+ "37 38 74.7 49.4 45.7 14.7\n",
256
+ "38 39 43.1 26.7 35.1 10.1\n",
257
+ "39 40 228.0 37.7 32.0 21.5\n",
258
+ "40 41 202.5 22.3 31.6 16.6\n",
259
+ "41 42 177.0 33.4 38.7 17.1\n",
260
+ "42 43 293.6 27.7 1.8 20.7\n",
261
+ "43 44 206.9 8.4 26.4 12.9\n",
262
+ "44 45 25.1 25.7 43.3 8.5\n",
263
+ "45 46 175.1 22.5 31.5 14.9\n",
264
+ "46 47 89.7 9.9 35.7 10.6\n",
265
+ "47 48 239.9 41.5 18.5 23.2\n",
266
+ "48 49 227.2 15.8 49.9 14.8\n",
267
+ "49 50 66.9 11.7 36.8 9.7\n",
268
+ "50 51 199.8 3.1 34.6 11.4\n",
269
+ "51 52 100.4 9.6 3.6 10.7\n",
270
+ "52 53 216.4 41.7 39.6 22.6\n",
271
+ "53 54 182.6 46.2 58.7 21.2\n",
272
+ "54 55 262.7 28.8 15.9 20.2\n",
273
+ "55 56 198.9 49.4 60.0 23.7\n",
274
+ "56 57 7.3 28.1 41.4 5.5\n",
275
+ "57 58 136.2 19.2 16.6 13.2\n",
276
+ "58 59 210.8 49.6 37.7 23.8\n",
277
+ "59 60 210.7 29.5 9.3 18.4\n",
278
+ "60 61 53.5 2.0 21.4 8.1\n",
279
+ "61 62 261.3 42.7 54.7 24.2\n",
280
+ "62 63 239.3 15.5 27.3 15.7\n",
281
+ "63 64 102.7 29.6 8.4 14.0\n",
282
+ "64 65 131.1 42.8 28.9 18.0\n",
283
+ "65 66 69.0 9.3 0.9 9.3\n",
284
+ "66 67 31.5 24.6 2.2 9.5\n",
285
+ "67 68 139.3 14.5 10.2 13.4\n",
286
+ "68 69 237.4 27.5 11.0 18.9\n",
287
+ "69 70 216.8 43.9 27.2 22.3\n",
288
+ "70 71 199.1 30.6 38.7 18.3\n",
289
+ "71 72 109.8 14.3 31.7 12.4\n",
290
+ "72 73 26.8 33.0 19.3 8.8\n",
291
+ "73 74 129.4 5.7 31.3 11.0\n",
292
+ "74 75 213.4 24.6 13.1 17.0\n",
293
+ "75 76 16.9 43.7 89.4 8.7\n",
294
+ "76 77 27.5 1.6 20.7 6.9\n",
295
+ "77 78 120.5 28.5 14.2 14.2\n",
296
+ "78 79 5.4 29.9 9.4 5.3\n",
297
+ "79 80 116.0 7.7 23.1 11.0\n",
298
+ "80 81 76.4 26.7 22.3 11.8\n",
299
+ "81 82 239.8 4.1 36.9 12.3\n",
300
+ "82 83 75.3 20.3 32.5 11.3\n",
301
+ "83 84 68.4 44.5 35.6 13.6\n",
302
+ "84 85 213.5 43.0 33.8 21.7\n",
303
+ "85 86 193.2 18.4 65.7 15.2\n",
304
+ "86 87 76.3 27.5 16.0 12.0\n",
305
+ "87 88 110.7 40.6 63.2 16.0\n",
306
+ "88 89 88.3 25.5 73.4 12.9\n",
307
+ "89 90 109.8 47.8 51.4 16.7\n",
308
+ "90 91 134.3 4.9 9.3 11.2\n",
309
+ "91 92 28.6 1.5 33.0 7.3\n",
310
+ "92 93 217.7 33.5 59.0 19.4\n",
311
+ "93 94 250.9 36.5 72.3 22.2\n",
312
+ "94 95 107.4 14.0 10.9 11.5\n",
313
+ "95 96 163.3 31.6 52.9 16.9\n",
314
+ "96 97 197.6 3.5 5.9 11.7\n",
315
+ "97 98 184.9 21.0 22.0 15.5\n",
316
+ "98 99 289.7 42.3 51.2 25.4\n",
317
+ "99 100 135.2 41.7 45.9 17.2\n",
318
+ "100 101 222.4 4.3 49.8 11.7\n",
319
+ "101 102 296.4 36.3 100.9 23.8\n",
320
+ "102 103 280.2 10.1 21.4 14.8\n",
321
+ "103 104 187.9 17.2 17.9 14.7\n",
322
+ "104 105 238.2 34.3 5.3 20.7\n",
323
+ "105 106 137.9 46.4 59.0 19.2\n",
324
+ "106 107 25.0 11.0 29.7 7.2\n",
325
+ "107 108 90.4 0.3 23.2 8.7\n",
326
+ "108 109 13.1 0.4 25.6 5.3\n",
327
+ "109 110 255.4 26.9 5.5 19.8\n",
328
+ "110 111 225.8 8.2 56.5 13.4\n",
329
+ "111 112 241.7 38.0 23.2 21.8\n",
330
+ "112 113 175.7 15.4 2.4 14.1\n",
331
+ "113 114 209.6 20.6 10.7 15.9\n",
332
+ "114 115 78.2 46.8 34.5 14.6\n",
333
+ "115 116 75.1 35.0 52.7 12.6\n",
334
+ "116 117 139.2 14.3 25.6 12.2\n",
335
+ "117 118 76.4 0.8 14.8 9.4\n",
336
+ "118 119 125.7 36.9 79.2 15.9\n",
337
+ "119 120 19.4 16.0 22.3 6.6\n",
338
+ "120 121 141.3 26.8 46.2 15.5\n",
339
+ "121 122 18.8 21.7 50.4 7.0\n",
340
+ "122 123 224.0 2.4 15.6 11.6\n",
341
+ "123 124 123.1 34.6 12.4 15.2\n",
342
+ "124 125 229.5 32.3 74.2 19.7\n",
343
+ "125 126 87.2 11.8 25.9 10.6\n",
344
+ "126 127 7.8 38.9 50.6 6.6\n",
345
+ "127 128 80.2 0.0 9.2 8.8\n",
346
+ "128 129 220.3 49.0 3.2 24.7\n",
347
+ "129 130 59.6 12.0 43.1 9.7\n",
348
+ "130 131 0.7 39.6 8.7 1.6\n",
349
+ "131 132 265.2 2.9 43.0 12.7\n",
350
+ "132 133 8.4 27.2 2.1 5.7\n",
351
+ "133 134 219.8 33.5 45.1 19.6\n",
352
+ "134 135 36.9 38.6 65.6 10.8\n",
353
+ "135 136 48.3 47.0 8.5 11.6\n",
354
+ "136 137 25.6 39.0 9.3 9.5\n",
355
+ "137 138 273.7 28.9 59.7 20.8\n",
356
+ "138 139 43.0 25.9 20.5 9.6\n",
357
+ "139 140 184.9 43.9 1.7 20.7\n",
358
+ "140 141 73.4 17.0 12.9 10.9\n",
359
+ "141 142 193.7 35.4 75.6 19.2\n",
360
+ "142 143 220.5 33.2 37.9 20.1\n",
361
+ "143 144 104.6 5.7 34.4 10.4\n",
362
+ "144 145 96.2 14.8 38.9 11.4\n",
363
+ "145 146 140.3 1.9 9.0 10.3\n",
364
+ "146 147 240.1 7.3 8.7 13.2\n",
365
+ "147 148 243.2 49.0 44.3 25.4\n",
366
+ "148 149 38.0 40.3 11.9 10.9\n",
367
+ "149 150 44.7 25.8 20.6 10.1\n",
368
+ "150 151 280.7 13.9 37.0 16.1\n",
369
+ "151 152 121.0 8.4 48.7 11.6\n",
370
+ "152 153 197.6 23.3 14.2 16.6\n",
371
+ "153 154 171.3 39.7 37.7 19.0\n",
372
+ "154 155 187.8 21.1 9.5 15.6\n",
373
+ "155 156 4.1 11.6 5.7 3.2\n",
374
+ "156 157 93.9 43.5 50.5 15.3\n",
375
+ "157 158 149.8 1.3 24.3 10.1\n",
376
+ "158 159 11.7 36.9 45.2 7.3\n",
377
+ "159 160 131.7 18.4 34.6 12.9\n",
378
+ "160 161 172.5 18.1 30.7 14.4\n",
379
+ "161 162 85.7 35.8 49.3 13.3\n",
380
+ "162 163 188.4 18.1 25.6 14.9\n",
381
+ "163 164 163.5 36.8 7.4 18.0\n",
382
+ "164 165 117.2 14.7 5.4 11.9\n",
383
+ "165 166 234.5 3.4 84.8 11.9\n",
384
+ "166 167 17.9 37.6 21.6 8.0\n",
385
+ "167 168 206.8 5.2 19.4 12.2\n",
386
+ "168 169 215.4 23.6 57.6 17.1\n",
387
+ "169 170 284.3 10.6 6.4 15.0\n",
388
+ "170 171 50.0 11.6 18.4 8.4\n",
389
+ "171 172 164.5 20.9 47.4 14.5\n",
390
+ "172 173 19.6 20.1 17.0 7.6\n",
391
+ "173 174 168.4 7.1 12.8 11.7\n",
392
+ "174 175 222.4 3.4 13.1 11.5\n",
393
+ "175 176 276.9 48.9 41.8 27.0\n",
394
+ "176 177 248.4 30.2 20.3 20.2\n",
395
+ "177 178 170.2 7.8 35.2 11.7\n",
396
+ "178 179 276.7 2.3 23.7 11.8\n",
397
+ "179 180 165.6 10.0 17.6 12.6\n",
398
+ "180 181 156.6 2.6 8.3 10.5\n",
399
+ "181 182 218.5 5.4 27.4 12.2\n",
400
+ "182 183 56.2 5.7 29.7 8.7\n",
401
+ "183 184 287.6 43.0 71.8 26.2\n",
402
+ "184 185 253.8 21.3 30.0 17.6\n",
403
+ "185 186 205.0 45.1 19.6 22.6\n",
404
+ "186 187 139.5 2.1 26.6 10.3\n",
405
+ "187 188 191.1 28.7 18.2 17.3\n",
406
+ "188 189 286.0 13.9 3.7 15.9\n",
407
+ "189 190 18.7 12.1 23.4 6.7\n",
408
+ "190 191 39.5 41.1 5.8 10.8\n",
409
+ "191 192 75.5 10.8 6.0 9.9\n",
410
+ "192 193 17.2 4.1 31.6 5.9\n",
411
+ "193 194 166.8 42.0 3.6 19.6\n",
412
+ "194 195 149.7 35.6 6.0 17.3\n",
413
+ "195 196 38.2 3.7 13.8 7.6\n",
414
+ "196 197 94.2 4.9 8.1 9.7\n",
415
+ "197 198 177.0 9.3 6.4 12.8\n",
416
+ "198 199 283.6 42.0 66.2 25.5\n",
417
+ "199 200 232.1 8.6 8.7 13.4\n"
418
+ ]
419
+ }
420
+ ],
421
+ "source": [
422
+ "dataset=pd.read_csv('Advertising.csv')\n",
423
+ "print(dataset.to_string())"
424
+ ]
425
+ },
426
+ {
427
+ "cell_type": "code",
428
+ "execution_count": 9,
429
+ "id": "7967d3ba",
430
+ "metadata": {},
431
+ "outputs": [
432
+ {
433
+ "name": "stdout",
434
+ "output_type": "stream",
435
+ "text": [
436
+ " Unnamed: 0 TV radio newspaper sales\n",
437
+ "190 191 39.5 41.1 5.8 10.8\n",
438
+ "191 192 75.5 10.8 6.0 9.9\n",
439
+ "192 193 17.2 4.1 31.6 5.9\n",
440
+ "193 194 166.8 42.0 3.6 19.6\n",
441
+ "194 195 149.7 35.6 6.0 17.3\n",
442
+ "195 196 38.2 3.7 13.8 7.6\n",
443
+ "196 197 94.2 4.9 8.1 9.7\n",
444
+ "197 198 177.0 9.3 6.4 12.8\n",
445
+ "198 199 283.6 42.0 66.2 25.5\n",
446
+ "199 200 232.1 8.6 8.7 13.4\n"
447
+ ]
448
+ }
449
+ ],
450
+ "source": [
451
+ "print(dataset.tail(10))"
452
+ ]
453
+ },
454
+ {
455
+ "cell_type": "code",
456
+ "execution_count": 11,
457
+ "id": "1347c424",
458
+ "metadata": {},
459
+ "outputs": [
460
+ {
461
+ "name": "stdout",
462
+ "output_type": "stream",
463
+ "text": [
464
+ "60\n"
465
+ ]
466
+ }
467
+ ],
468
+ "source": [
469
+ "print(pd.options.display.max_rows)"
470
+ ]
471
+ },
472
+ {
473
+ "cell_type": "code",
474
+ "execution_count": 12,
475
+ "id": "0053340d",
476
+ "metadata": {},
477
+ "outputs": [
478
+ {
479
+ "name": "stdout",
480
+ "output_type": "stream",
481
+ "text": [
482
+ "<class 'pandas.core.frame.DataFrame'>\n",
483
+ "RangeIndex: 200 entries, 0 to 199\n",
484
+ "Data columns (total 5 columns):\n",
485
+ " # Column Non-Null Count Dtype \n",
486
+ "--- ------ -------------- ----- \n",
487
+ " 0 Unnamed: 0 200 non-null int64 \n",
488
+ " 1 TV 200 non-null float64\n",
489
+ " 2 radio 200 non-null float64\n",
490
+ " 3 newspaper 200 non-null float64\n",
491
+ " 4 sales 200 non-null float64\n",
492
+ "dtypes: float64(4), int64(1)\n",
493
+ "memory usage: 7.9 KB\n",
494
+ "None\n"
495
+ ]
496
+ }
497
+ ],
498
+ "source": [
499
+ "print(dataset.info())\n"
500
+ ]
501
+ },
502
+ {
503
+ "cell_type": "code",
504
+ "execution_count": 13,
505
+ "id": "ed6b5c85",
506
+ "metadata": {},
507
+ "outputs": [
508
+ {
509
+ "name": "stdout",
510
+ "output_type": "stream",
511
+ "text": [
512
+ "<class 'pandas.core.frame.DataFrame'>\n",
513
+ "RangeIndex: 101 entries, 0 to 100\n",
514
+ "Data columns (total 18 columns):\n",
515
+ " # Column Non-Null Count Dtype \n",
516
+ "--- ------ -------------- ----- \n",
517
+ " 0 animal_name 101 non-null object\n",
518
+ " 1 hair 101 non-null int64 \n",
519
+ " 2 feathers 101 non-null int64 \n",
520
+ " 3 eggs 101 non-null int64 \n",
521
+ " 4 milk 101 non-null int64 \n",
522
+ " 5 airborne 101 non-null int64 \n",
523
+ " 6 aquatic 101 non-null int64 \n",
524
+ " 7 predator 101 non-null int64 \n",
525
+ " 8 toothed 101 non-null int64 \n",
526
+ " 9 backbone 101 non-null int64 \n",
527
+ " 10 breathes 101 non-null int64 \n",
528
+ " 11 venomous 101 non-null int64 \n",
529
+ " 12 fins 101 non-null int64 \n",
530
+ " 13 legs 101 non-null int64 \n",
531
+ " 14 tail 101 non-null int64 \n",
532
+ " 15 domestic 101 non-null int64 \n",
533
+ " 16 catsize 101 non-null int64 \n",
534
+ " 17 class_type 101 non-null int64 \n",
535
+ "dtypes: int64(17), object(1)\n",
536
+ "memory usage: 14.3+ KB\n",
537
+ "None\n"
538
+ ]
539
+ }
540
+ ],
541
+ "source": [
542
+ "print(dataset2.info())"
543
+ ]
544
+ },
545
+ {
546
+ "cell_type": "code",
547
+ "execution_count": 14,
548
+ "id": "109d99a3",
549
+ "metadata": {},
550
+ "outputs": [
551
+ {
552
+ "name": "stdout",
553
+ "output_type": "stream",
554
+ "text": [
555
+ "23.264000000000024\n"
556
+ ]
557
+ }
558
+ ],
559
+ "source": [
560
+ "print(dataset['radio'].mean())"
561
+ ]
562
+ },
563
+ {
564
+ "cell_type": "code",
565
+ "execution_count": 15,
566
+ "id": "2ff8fd70",
567
+ "metadata": {},
568
+ "outputs": [
569
+ {
570
+ "name": "stdout",
571
+ "output_type": "stream",
572
+ "text": [
573
+ "0 4.1\n",
574
+ "1 5.7\n",
575
+ "Name: radio, dtype: float64\n"
576
+ ]
577
+ }
578
+ ],
579
+ "source": [
580
+ "print(dataset['radio'].mode())"
581
+ ]
582
+ },
583
+ {
584
+ "cell_type": "code",
585
+ "execution_count": null,
586
+ "id": "ec82cd0a",
587
+ "metadata": {},
588
+ "outputs": [],
589
+ "source": []
590
+ },
591
+ {
592
+ "cell_type": "code",
593
+ "execution_count": 16,
594
+ "id": "fd136034",
595
+ "metadata": {},
596
+ "outputs": [
597
+ {
598
+ "name": "stdout",
599
+ "output_type": "stream",
600
+ "text": [
601
+ "22.9\n"
602
+ ]
603
+ }
604
+ ],
605
+ "source": [
606
+ "print(dataset['radio'].median())"
607
+ ]
608
+ },
609
+ {
610
+ "cell_type": "code",
611
+ "execution_count": 1,
612
+ "id": "b0f24bb1",
613
+ "metadata": {},
614
+ "outputs": [],
615
+ "source": [
616
+ "import pandas as pd"
617
+ ]
618
+ },
619
+ {
620
+ "cell_type": "code",
621
+ "execution_count": 7,
622
+ "id": "31734cdf",
623
+ "metadata": {},
624
+ "outputs": [],
625
+ "source": [
626
+ "import matplotlib as mt"
627
+ ]
628
+ },
629
+ {
630
+ "cell_type": "code",
631
+ "execution_count": 19,
632
+ "id": "682ce274",
633
+ "metadata": {},
634
+ "outputs": [
635
+ {
636
+ "name": "stdout",
637
+ "output_type": "stream",
638
+ "text": [
639
+ " Duration Pulse Maxpulse Calories\n",
640
+ "0 60 110 130 409.1\n",
641
+ "1 60 117 145 479.0\n",
642
+ "2 60 103 135 340.0\n",
643
+ "3 45 109 175 282.4\n",
644
+ "4 45 117 148 406.0\n"
645
+ ]
646
+ },
647
+ {
648
+ "ename": "AttributeError",
649
+ "evalue": "module 'matplotlib' has no attribute 'show'",
650
+ "output_type": "error",
651
+ "traceback": [
652
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
653
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
654
+ "Input \u001b[0;32mIn [19]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mhead())\n\u001b[1;32m 3\u001b[0m df\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscatter\u001b[39m\u001b[38;5;124m'\u001b[39m, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDuration\u001b[39m\u001b[38;5;124m'\u001b[39m,y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMaxpulse\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m \u001b[43mmt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m()\n",
655
+ "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/matplotlib/_api/__init__.py:222\u001b[0m, in \u001b[0;36mcaching_module_getattr.<locals>.__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m props:\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m props[name]\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__get__\u001b[39m(instance)\n\u001b[0;32m--> 222\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 223\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__module__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
656
+ "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib' has no attribute 'show'"
657
+ ]
658
+ },
659
+ {
660
+ "data": {
661
+ "image/png": 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\n",
662
+ "text/plain": [
663
+ "<Figure size 432x288 with 1 Axes>"
664
+ ]
665
+ },
666
+ "metadata": {
667
+ "needs_background": "light"
668
+ },
669
+ "output_type": "display_data"
670
+ }
671
+ ],
672
+ "source": [
673
+ "df=pd.read_csv('data.csv')\n",
674
+ "print(df.head())\n",
675
+ "df.plot(kind='scatter', x='Duration',y='Maxpulse')\n",
676
+ "mt.show()"
677
+ ]
678
+ },
679
+ {
680
+ "cell_type": "code",
681
+ "execution_count": 21,
682
+ "id": "297abc8e",
683
+ "metadata": {},
684
+ "outputs": [],
685
+ "source": [
686
+ "df2=pd.read_csv('loan_data.csv')"
687
+ ]
688
+ },
689
+ {
690
+ "cell_type": "code",
691
+ "execution_count": 22,
692
+ "id": "5c902c2a",
693
+ "metadata": {},
694
+ "outputs": [
695
+ {
696
+ "name": "stdout",
697
+ "output_type": "stream",
698
+ "text": [
699
+ " model mpg cyl disp hp drat wt qsec vs am gear \\\n",
700
+ "0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 \n",
701
+ "1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 \n",
702
+ "2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 \n",
703
+ "3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 \n",
704
+ "4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 \n",
705
+ "\n",
706
+ " carb \n",
707
+ "0 4 \n",
708
+ "1 4 \n",
709
+ "2 1 \n",
710
+ "3 1 \n",
711
+ "4 2 \n"
712
+ ]
713
+ }
714
+ ],
715
+ "source": [
716
+ "print(df2.head())"
717
+ ]
718
+ },
719
+ {
720
+ "cell_type": "code",
721
+ "execution_count": 23,
722
+ "id": "a8d6cd28",
723
+ "metadata": {},
724
+ "outputs": [
725
+ {
726
+ "name": "stdout",
727
+ "output_type": "stream",
728
+ "text": [
729
+ "<class 'pandas.core.frame.DataFrame'>\n",
730
+ "RangeIndex: 32 entries, 0 to 31\n",
731
+ "Data columns (total 12 columns):\n",
732
+ " # Column Non-Null Count Dtype \n",
733
+ "--- ------ -------------- ----- \n",
734
+ " 0 model 32 non-null object \n",
735
+ " 1 mpg 32 non-null float64\n",
736
+ " 2 cyl 32 non-null int64 \n",
737
+ " 3 disp 32 non-null float64\n",
738
+ " 4 hp 32 non-null int64 \n",
739
+ " 5 drat 32 non-null float64\n",
740
+ " 6 wt 32 non-null float64\n",
741
+ " 7 qsec 32 non-null float64\n",
742
+ " 8 vs 32 non-null int64 \n",
743
+ " 9 am 32 non-null int64 \n",
744
+ " 10 gear 32 non-null int64 \n",
745
+ " 11 carb 32 non-null int64 \n",
746
+ "dtypes: float64(5), int64(6), object(1)\n",
747
+ "memory usage: 3.1+ KB\n",
748
+ "None\n"
749
+ ]
750
+ }
751
+ ],
752
+ "source": [
753
+ "print(df2.info())"
754
+ ]
755
+ },
756
+ {
757
+ "cell_type": "code",
758
+ "execution_count": null,
759
+ "id": "7cce9436",
760
+ "metadata": {},
761
+ "outputs": [],
762
+ "source": [
763
+ "df2"
764
+ ]
765
+ }
766
+ ],
767
+ "metadata": {
768
+ "kernelspec": {
769
+ "display_name": "Python 3 (ipykernel)",
770
+ "language": "python",
771
+ "name": "python3"
772
+ },
773
+ "language_info": {
774
+ "codemirror_mode": {
775
+ "name": "ipython",
776
+ "version": 3
777
+ },
778
+ "file_extension": ".py",
779
+ "mimetype": "text/x-python",
780
+ "name": "python",
781
+ "nbconvert_exporter": "python",
782
+ "pygments_lexer": "ipython3",
783
+ "version": "3.9.12"
784
+ }
785
+ },
786
+ "nbformat": 4,
787
+ "nbformat_minor": 5
788
+ }