JIYIK CN >

pandas.DataFrame.replace() replaces values ​​in a DataFrame with other values, which can be strings, regular expressions, lists, dictionaries, Seriesor numbers.

pandas.DataFrame.replace()grammar

DataFrame.replace(,
                  to_replace=None,
                  value=None,
                  inplace=False,
                  limit=None,
                  regex=False,
                  method='pad')

parameter

Return Value

It returns a that replaces all specified fields DataFramewith the given .value

Example Code: pandas.DataFrame.replace()Replace values ​​in a DataFrame using

import pandas as pd
df = pd.DataFrame({'X': [1, 2, 3,],
                   'Y': [4, 1, 8]})
print("Before Replacement")
print(df)  
replaced_df=df.replace(1, 5)
print("After Replacement")
print(replaced_df)

Output:

Before Replacement
   X  Y
0  1  4
1  2  1
2  3  8
After Replacement
   X  Y
0  5  4
1  2  5
2  3  8

Here, 1represents to_replacethe parameter and 5represents the parameter replace()in the method value. Therefore, in df, all 1elements with value of are 5replaced by .

pandas.DataFrame.replace()Example Code: Replace Multiple Values ​​in a DataFrame Using

Using List Replacement

import pandas as pd
df = pd.DataFrame({'X': [1, 2, 3,],
                   'Y': [4, 1, 8]})
print("Before Replacement")
print(df)  
replaced_df=df.replace([1,2,3],[1,4,9])
print("After Replacement")
print(replaced_df)

Output:

Before Replacement
   X  Y
0  1  4
1  2  1
2  3  8
After Replacement
   X  Y
0  1  4
1  4  1
2  9  8

Here, [1,2,3]represents to_replacethe parameter and [1,4,9]represents the parameter replace()in the method value. Therefore, in df, the column [1,2,3]is [1,4,9]replaced by .

Use dictionary replacement

import pandas as pd
df = pd.DataFrame({'X': [1, 2, 3,],
                   'Y': [3, 1, 8]})
print("Before Replacement")
print(df)  
replaced_df=df.replace({1:10,3:30})
print("After Replacement")
print(replaced_df)

Output:

Before Replacement
   X  Y
0  1  3
1  2  1
2  3  8
After Replacement
    X   Y
0  10  30
1   2  10
2  30   8

1It replaces all elements whose value is with , and replaces 10all elements whose value is with .330

Use RegexReplace

import pandas as pd
df = pd.DataFrame({'X': ["zeppy", "amid", "amily"],
                   'Y': ["xar", "abc", "among"]})
print("Before Replacement")
print(df)  
df.replace(to_replace=r'^ami.$', value='song', regex=True,inplace=True)
print("After Replacement")
print(df)

Output:

Before Replacement
       X      Y
0  zeppy    xar
1   amid    abc
2  amily  among
After Replacement
       X      Y
0  zeppy    xar
1   song    abc
2  amily  among

It replaces all elements whose first three characters are amifollowed by any character with song. Here only amidsatisfies the given regular expression, so only is replaced amidby song. Although amilythe first three characters of are ami, amithere are two characters after . Therefore, amilydoes not satisfy the given regex, so it remains the same and is not replaced. If you are using regular expressions, make sure regexthat is set to True, and modify the original after inplace=Truecalling the method .replace()DataFrame