General usage of pandas data processing | apply() function

This article introduces about Pandas in apply() Several common uses of functions ,apply() The degree of freedom of the function is high , It can be directly to Series perhaps DataFrame Element by element traversal operation , Convenient and efficient , Similar to Numpy Characteristics of .

apply() When using , Usually put one lambda Function expression 、 Or a function as an operation , Officially apply() usage ：

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


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• • 
    
• func Represents the incoming function or lambda expression ;
• axis There are two parameters that can be provided , This parameter defaults to 0/ Column
• 0 perhaps index , Indicates that the function deals with each column ;
• 1 or columns , Indicates that each line is processed ;
• raw ;bool type , The default is False;
• False , Means to treat each row or column as Series In the incoming function ;
• True, Accept is ndarray data type ;
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apply() Finally, it is processed by function , The data to Series or DataFrame Format return .

Here are a few examples apply() Specific use of ;

DataFrame Use apply()

1, Calculate the square root of each element

This is just for convenience , Directly use numpy Of sqrt function ;

>>> df =pd.DataFrame([[4,9]]*3,columns = ['A','B'])

>>> df

 A B

0 4 9

1 4 9

2 4 9





>>> df.apply(np.sqrt)

 A B

0 2.0 3.0

1 2.0 3.0

2 2.0 3.0


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2, Calculate the average value of each line of elements

Here, the incoming data exists in the form of columns , therefore axis = 0, You can omit ;

>>> df.apply(np.mean)

A 4.0

B 9.0


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3, Calculate the average value of each column of elements

And 2 The difference is that it is passed in the form of rows , To add a parameter axis =1;

>>> df.apply(np.mean,axis = 1)

0 6.5

1 6.5

2 6.5

dtype: float64


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4, Add new column C, The values are columns A、B The sum of the

Implement this function , The simplest line of code can be achieved :

df['C'] = df.A +df.B


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But here we need apply() To achieve , Realize the usage of inter column operation , The operation steps are divided into the following two steps ：

1, First define a function implementation Column A + Column B ;

2, utilize apply() Add this function , And the data needs Join line by line , So set axis = 1

>>> def Add_a(x):

... return x.A+x.B



>>> df['C'] = df.apply(Add_a,axis=1)

>>> df

 A B C

0 4 9 13

1 4 9 13

2 4 9 13


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Series Use apply()

Series Use apply() Function and DataFrame be similar , The biggest difference in usage is the addition of a column name DataFram. Class name

1, Column A Add 1

no need apply() Methods

df.A =df.A +1


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utilize apply() Function to operate , Here I introduce a lambda function ：

>>> df.A = df.A.apply(lambda x:x+1)

>>> df

 A B C

0 5 9 13

1 5 9 13

2 5 9 13


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2, Judgment column A Whether the element in can be 2 to be divisible by , use Yes or No Mark beside

>>> df.A = df.A.apply(lambda x:str(x)+"\tYes" if x%2==0 else str(x)+"\tNo")

>>> df

 A B

0 5\tNo 9

1 5\tNo 9

2 5\tNo 9


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apply() Most usages of are the above points , The examples listed here are simpler , But it is enough for basic usage understanding .

That's all of this , Finally, thank you for reading ！