1+ {
2+ "nbformat" : 4 ,
3+ "nbformat_minor" : 0 ,
4+ "metadata" : {
5+ "colab" : {
6+ "name" : " apply.ipynb"
7+ "provenance" : [],
8+ "include_colab_link" : true
9+ },
10+ "kernelspec" : {
11+ "name" : " python3"
12+ "display_name" : " Python 3"
13+ }
14+ },
15+ "cells" : [
16+ {
17+ "cell_type" : " markdown"
18+ "metadata" : {
19+ "id" : " view-in-github"
20+ "colab_type" : " text"
21+ },
22+ "source" : [
23+ " <a href=\" https://colab.research.google.com/github/tinhb92/relax_ml/blob/master/pandas/apply.ipynb\" target=\" _parent\" ><img src=\" https://colab.research.google.com/assets/colab-badge.svg\" alt=\" Open In Colab\" /></a>"
24+ ]
25+ },
26+ {
27+ "cell_type" : " code"
28+ "metadata" : {
29+ "id" : " AYIHr7yMISYZ"
30+ "colab_type" : " code"
31+ "colab" : {}
32+ },
33+ "source" : [
34+ " import numpy as np\n "
35+ " import pandas as pd"
36+ ],
37+ "execution_count" : 0 ,
38+ "outputs" : []
39+ },
40+ {
41+ "cell_type" : " code"
42+ "metadata" : {
43+ "id" : " bnE39B78HxDL"
44+ "colab_type" : " code"
45+ "colab" : {}
46+ },
47+ "source" : [
48+ " df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])"
49+ ],
50+ "execution_count" : 0 ,
51+ "outputs" : []
52+ },
53+ {
54+ "cell_type" : " code"
55+ "metadata" : {
56+ "id" : " 96-jbKvaIM5l"
57+ "colab_type" : " code"
58+ "colab" : {
59+ "base_uri" : " https://localhost:8080/"
60+ "height" : 136
61+ },
62+ "outputId" : " 00f3c564-368a-4b0a-ccc5-2a8dadd938a6"
63+ },
64+ "source" : [
65+ " df.apply(np.sqrt)"
66+ ],
67+ "execution_count" : 7 ,
68+ "outputs" : [
69+ {
70+ "output_type" : " execute_result"
71+ "data" : {
72+ "text/html" : [
73+ " <div>\n "
74+ " <style scoped>\n "
75+ " .dataframe tbody tr th:only-of-type {\n "
76+ " vertical-align: middle;\n "
77+ " }\n "
78+ " \n "
79+ " .dataframe tbody tr th {\n "
80+ " vertical-align: top;\n "
81+ " }\n "
82+ " \n "
83+ " .dataframe thead th {\n "
84+ " text-align: right;\n "
85+ " }\n "
86+ " </style>\n "
87+ " <table border=\" 1\" class=\" dataframe\" >\n "
88+ " <thead>\n "
89+ " <tr style=\" text-align: right;\" >\n "
90+ " <th></th>\n "
91+ " <th>A</th>\n "
92+ " <th>B</th>\n "
93+ " </tr>\n "
94+ " </thead>\n "
95+ " <tbody>\n "
96+ " <tr>\n "
97+ " <th>0</th>\n "
98+ " <td>2.0</td>\n "
99+ " <td>3.0</td>\n "
100+ " </tr>\n "
101+ " <tr>\n "
102+ " <th>1</th>\n "
103+ " <td>2.0</td>\n "
104+ " <td>3.0</td>\n "
105+ " </tr>\n "
106+ " <tr>\n "
107+ " <th>2</th>\n "
108+ " <td>2.0</td>\n "
109+ " <td>3.0</td>\n "
110+ " </tr>\n "
111+ " </tbody>\n "
112+ " </table>\n "
113+ " </div>"
114+ ],
115+ "text/plain" : [
116+ " A B\n "
117+ " 0 2.0 3.0\n "
118+ " 1 2.0 3.0\n "
119+ " 2 2.0 3.0"
120+ ]
121+ },
122+ "metadata" : {
123+ "tags" : []
124+ },
125+ "execution_count" : 7
126+ }
127+ ]
128+ },
129+ {
130+ "cell_type" : " code"
131+ "metadata" : {
132+ "id" : " puajjOwmIUM5"
133+ "colab_type" : " code"
134+ "colab" : {
135+ "base_uri" : " https://localhost:8080/"
136+ "height" : 84
137+ },
138+ "outputId" : " 70abfc24-77d2-42a2-8442-d47208d84377"
139+ },
140+ "source" : [
141+ " df.apply(lambda x: [1, 2], axis=1)"
142+ ],
143+ "execution_count" : 8 ,
144+ "outputs" : [
145+ {
146+ "output_type" : " execute_result"
147+ "data" : {
148+ "text/plain" : [
149+ " 0 [1, 2]\n "
150+ " 1 [1, 2]\n "
151+ " 2 [1, 2]\n "
152+ " dtype: object"
153+ ]
154+ },
155+ "metadata" : {
156+ "tags" : []
157+ },
158+ "execution_count" : 8
159+ }
160+ ]
161+ },
162+ {
163+ "cell_type" : " code"
164+ "metadata" : {
165+ "id" : " qfJ9kOeZIbSM"
166+ "colab_type" : " code"
167+ "colab" : {}
168+ },
169+ "source" : [
170+ " "
171+ ],
172+ "execution_count" : 0 ,
173+ "outputs" : []
174+ }
175+ ]
176+ }
0 commit comments