pandas函数应用

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