Python apply function

Python apply function

 

1 Introduction

apply function is pandas which all functions in the function of the highest degree of freedom. The function is as follows:

DataFrame.apply(func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds)

The most useful function is the first parameter, the parameter is a function, the function pointers corresponding to C / C ++ is.

This function needs its own implementation arguments passed to the function depends axis, such as axis = 1, will put his data as a data structure passed to the function Series of their own implementation, we realize the different attributes of the Series in the function Computation of the returns a result, the apply function automatically through each row DataFrame data, and finally combining all the results into a data structure and returns Series.

2, Sample

import numpy as np
import pandas as pd


f = lambda x: x.max()-x.min()

df = pd.DataFrame(np.random.randn(4,3),columns=list('bde'),index=['utah', 'ohio', 'texas', 'oregon'])
print(df)

t1 = df.apply(f)
print(t1)

t2 = df.apply(f, axis=1)
print(t2)

 

The output is shown below:

               b         d         e
utah    1.106486  0.101113 -0.494279
ohio    0.955676 -1.889499  0.522151
texas   1.891144 -0.670588  0.106530
oregon -0.062372  0.991231  0.294464

b    1.953516
d    2.880730
e    1.016430
dtype: float64

utah      1.600766
ohio      2.845175
texas     2.561732
oregon    1.053603
dtype: float64

 

3, performance comparison

df = pd.DataFrame({'a': np.random.randn(6),
                   'b': ['foo', 'bar'] * 3,
                   'c': np.random.randn(6)})


def my_test(a, b):
    return a + b


print(df)


df['Value'] = df.apply(lambda row: my_test(row['a'], row['c']), axis=1) # Method. 1 
Print (DF) 

DF [ ' Value2 ' ] = DF [ ' A ' ] + DF [ ' C ' ]   # Method 2 
Print (DF)

 

Output:

          a    b         c
0 -1.194841  foo  1.648214
1 -0.377554  bar  0.496678
2  1.524940  foo -1.245333
3 -0.248150  bar  1.526515
4  0.283395  foo  1.282233
5  0.117674  bar -0.094462

          a    b         c     Value
0 -1.194841  foo  1.648214  0.453374
1 -0.377554  bar  0.496678  0.119124
2  1.524940  foo -1.245333  0.279607
3 -0.248150  bar  1.526515  1.278365
4  0.283395  foo  1.282233  1.565628
5  0.117674  bar -0.094462  0.023212

          a    b         c     Value    Value2
0 -1.194841  foo  1.648214  0.453374  0.453374
1 -0.377554  bar  0.496678  0.119124  0.119124
2  1.524940  foo -1.245333  0.279607  0.279607
3 -0.248150  bar  1.526515  1.278365  1.278365
4  0.283395  foo  1.282233  1.565628  1.565628
5  0.117674  bar -0.094462  0.023212  0.023212

 

NOTE: When a large amount of data, for simple logic Solution method 2 (hundreds of M processing personal data set, when method 1 is about 200S flowers, flower when 10s Method 2)! ! !

1 Introduction