高阶函数处理
- 在dataframe中使用apply方法，调用自定义函数对数据进行处理
- 函数apply，注意axis
- 可以使用astype函数对数据进行转换
- 可以使用map函数进行数据转换
import pandas as pd
import numpy as np
import osos.getcwd()'D:\\Jupyter\\notebook\\Python数据清洗实战\\数据清洗之数据转换'os.chdir('D:\\Jupyter\\notebook\\Python数据清洗实战\\数据')df = pd.read_csv('sam_tianchi_mum_baby.csv', dtype=str, encoding='utf-8')df.head(5)<div>
<style scoped>
.dataframe tbody tr th:only-of-type { vertical-align: middle;}.dataframe tbody tr th { vertical-align: top;}.dataframe thead th { text-align: right;}</style>
<table border=”1″ class=”dataframe”>
<thead>
<tr style="text-align: right;"> <th></th> <th>user_id</th> <th>birthday</th> <th>gender</th></tr></thead>
<tbody>
<tr> <th>0</th> <td>2757</td> <td>20130311</td> <td>1</td></tr><tr> <th>1</th> <td>415971</td> <td>20121111</td> <td>0</td></tr><tr> <th>2</th> <td>1372572</td> <td>20120130</td> <td>1</td></tr><tr> <th>3</th> <td>10339332</td> <td>20110910</td> <td>0</td></tr><tr> <th>4</th> <td>10642245</td> <td>20130213</td> <td>0</td></tr></tbody>
</table>
</div>
def f(x):
if '0' in str(x):
return '女'
elif '1' in str(x):
return '男'
else:
return '未知'# apply函数可做很多其他处理
df['性别'] = df['gender'].apply(f)df.head(5)<div>
<style scoped>
.dataframe tbody tr th:only-of-type { vertical-align: middle;}.dataframe tbody tr th { vertical-align: top;}.dataframe thead th { text-align: right;}</style>
<table border=”1″ class=”dataframe”>
<thead>
<tr style="text-align: right;"> <th></th> <th>user_id</th> <th>birthday</th> <th>gender</th> <th>性别</th></tr></thead>
<tbody>
<tr> <th>0</th> <td>2757</td> <td>20130311</td> <td>1</td> <td>男</td></tr><tr> <th>1</th> <td>415971</td> <td>20121111</td> <td>0</td> <td>女</td></tr><tr> <th>2</th> <td>1372572</td> <td>20120130</td> <td>1</td> <td>男</td></tr><tr> <th>3</th> <td>10339332</td> <td>20110910</td> <td>0</td> <td>女</td></tr><tr> <th>4</th> <td>10642245</td> <td>20130213</td> <td>0</td> <td>女</td></tr></tbody>
</table>
</div>
# 查看性别为未知数据
df[df['gender'] == '2'].head(5)<div>
<style scoped>
.dataframe tbody tr th:only-of-type { vertical-align: middle;}.dataframe tbody tr th { vertical-align: top;}.dataframe thead th { text-align: right;}</style>
<table border=”1″ class=”dataframe”>
<thead>
<tr style="text-align: right;"> <th></th> <th>user_id</th> <th>birthday</th> <th>gender</th> <th>性别</th></tr></thead>
<tbody>
<tr> <th>46</th> <td>49167150</td> <td>20130818</td> <td>2</td> <td>未知</td></tr><tr> <th>47</th> <td>49983255</td> <td>20140206</td> <td>2</td> <td>未知</td></tr><tr> <th>51</th> <td>52529655</td> <td>20130611</td> <td>2</td> <td>未知</td></tr><tr> <th>58</th> <td>57711375</td> <td>20130420</td> <td>2</td> <td>未知</td></tr><tr> <th>106</th> <td>99665637</td> <td>20130926</td> <td>2</td> <td>未知</td></tr></tbody>
</table>
</div>
del df['性别']# map函数主要用于映射
df['性别'] = df['gender'].map({'0': '女性', '1':'男性', '2': '未知'})df.head(5)<div>
<style scoped>
.dataframe tbody tr th:only-of-type { vertical-align: middle;}.dataframe tbody tr th { vertical-align: top;}.dataframe thead th { text-align: right;}</style>
<table border=”1″ class=”dataframe”>
<thead>
<tr style="text-align: right;"> <th></th> <th>user_id</th> <th>birthday</th> <th>gender</th> <th>性别</th></tr></thead>
<tbody>
<tr> <th>0</th> <td>2757</td> <td>20130311</td> <td>1</td> <td>男性</td></tr><tr> <th>1</th> <td>415971</td> <td>20121111</td> <td>0</td> <td>女性</td></tr><tr> <th>2</th> <td>1372572</td> <td>20120130</td> <td>1</td> <td>男性</td></tr><tr> <th>3</th> <td>10339332</td> <td>20110910</td> <td>0</td> <td>女性</td></tr><tr> <th>4</th> <td>10642245</td> <td>20130213</td> <td>0</td> <td>女性</td></tr></tbody>
</table>
</div>
del df['性别']# map函数也可传入自己定义的函数
df['性别'] = df['gender'].map(f)df.head(5)<div>
<style scoped>
.dataframe tbody tr th:only-of-type { vertical-align: middle;}.dataframe tbody tr th { vertical-align: top;}.dataframe thead th { text-align: right;}</style>
<table border=”1″ class=”dataframe”>
<thead>
<tr style="text-align: right;"> <th></th> <th>user_id</th> <th>birthday</th> <th>gender</th> <th>性别</th></tr></thead>
<tbody>
<tr> <th>0</th> <td>2757</td> <td>20130311</td> <td>1</td> <td>男</td></tr><tr> <th>1</th> <td>415971</td> <td>20121111</td> <td>0</td> <td>女</td></tr><tr> <th>2</th> <td>1372572</td> <td>20120130</td> <td>1</td> <td>男</td></tr><tr> <th>3</th> <td>10339332</td> <td>20110910</td> <td>0</td> <td>女</td></tr><tr> <th>4</th> <td>10642245</td> <td>20130213</td> <td>0</td> <td>女</td></tr></tbody>
</table>
</div>
# 脱敏处理
# 可使用lambda函数
df['user_id'].apply(lambda x: str(x).replace(x[1:3], '**')).head(5)0 2**71 4**9712 1**25723 1**393324 1**42245Name: user_id, dtype: objectdf['birthday'].apply(lambda x: x[0:4]).head(5)0 20131 20122 20123 20114 2013Name: birthday, dtype: object正文完