1、管道函数
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/5/24 15:03
# @Author : zhang chao
# @File : s.py #pipe管道函数的应用
import pandas as pd
import numpy as np def adder(ele1,ele2):
return ele1+ele2 df = pd.DataFrame(np.random.randn(5,3),columns=['col1','col2','col3'])
print(df)
df2=df.pipe(adder,2)#df中每一个元素都加2
print('-'*100)
print("df.pipe(adder,2) df中每一个元素都加2")
print (df2) D:\Download\python3\python3.exe D:/Download/pycharmworkspace/s.py
col1 col2 col3
0 -0.541685 -1.009440 -1.680244
1 -0.881437 0.022469 0.911686
2 0.930035 1.073783 0.096894
3 -1.282204 -0.039941 0.147482
4 -1.743847 -1.187832 -0.402219
----------------------------------------------------------------------------------------------------
df.pipe(adder,2) df中每一个元素都加2
col1 col2 col3
0 1.458315 0.990560 0.319756
1 1.118563 2.022469 2.911686
2 2.930035 3.073783 2.096894
3 0.717796 1.960059 2.147482
4 0.256153 0.812168 1.597781 Process finished with exit code 0
2、
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/5/24 15:03
# @Author : zhang chao
# @File : s.py #可以使用apply()方法沿DataFrame或Panel的轴应用任意函数，它与描述性统计方法一样，采用可选的轴参数。
# 默认情况下，操作按列执行，将每列列为数组。
import pandas as pd
import numpy as np df = pd.DataFrame(np.random.randn(5,3),columns=['col1','col2','col3'])
print (df)
print('-'*100)
print("df1=df.apply(np.mean)=df.apply(np.mean,axis=0) 默认按列执行操作:")
df1=df.apply(np.mean)
print (df1)
print('-'*100)
print("df2=df.apply(np.mean,axis=1) 按行执行操作:")
df2=df.apply(np.mean,axis=1)
print (df2)
print('-'*100)
df3=df.apply(lambda x: x.max() - x.min())
print("df3=df.apply(lambda x: x.max() - x.min()):")
print (df3)
print('-'*100)
df4=df['col1'].map(lambda x:x*100)
print("df4=df['col1'].map(lambda x:x*100):")
print (df4)
print('-'*100)
df5=df.applymap(lambda x:x*100)
print("df5=df.applymap(lambda x:x*100):")
print (df5) D:\Download\python3\python3.exe D:/Download/pycharmworkspace/s.py
col1 col2 col3
0 0.735342 0.438729 -0.261747
1 -1.490907 0.397943 0.105613
2 -0.298617 -0.328284 0.599502
3 -0.842654 0.324976 -0.047985
4 0.452950 1.102824 0.023971
----------------------------------------------------------------------------------------------------
df1=df.apply(np.mean)=df.apply(np.mean,axis=0) 默认按列执行操作:
col1 -0.288777
col2 0.387238
col3 0.083871
dtype: float64
----------------------------------------------------------------------------------------------------
df2=df.apply(np.mean,axis=1) 按行执行操作:
0 0.304108
1 -0.329117
2 -0.009133
3 -0.188555
4 0.526582
dtype: float64
----------------------------------------------------------------------------------------------------
df3=df.apply(lambda x: x.max() - x.min()):
col1 2.226249
col2 1.431108
col3 0.861248
dtype: float64
----------------------------------------------------------------------------------------------------
df4=df['col1'].map(lambda x:x*100):
0 73.534186
1 -149.090744
2 -29.861721
3 -84.265380
4 45.295040
Name: col1, dtype: float64
----------------------------------------------------------------------------------------------------
df5=df.applymap(lambda x:x*100):
col1 col2 col3
0 73.534186 43.872940 -26.174660
1 -149.090744 39.794331 10.561263
2 -29.861721 -32.828359 59.950153
3 -84.265380 32.497553 -4.798542
4 45.295040 110.282391 2.397062 Process finished with exit code 0