Pandas 將列值轉換為字串

Suraj Joshi 2023年1月30日 2021年1月22日 Pandas Pandas DataFrame Column
  1. 使用 apply() 方法將 DataFrame 的列值的資料型別轉換為字串
  2. 使用 applymap() 方法將所有 DataFrame 列的資料型別轉換為 string
  3. 使用 astype() 方法將 DataFrame 列值的資料型別轉換為 string
Pandas 將列值轉換為字串

本教程介紹瞭如何將 DataFrame 的列值的資料型別轉換為字串。

import pandas as pd

employees_df = pd.DataFrame({
    'Name': ["Ayush","Bikram","Ceela","Kusal","Shanty"],
    'Score': [31, 38, 33, 39,35],
    'Age':  [33,34,38,45,37],

})

print(employees_df)

輸出:

     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37

我們將使用上面例子中顯示的 DataFrame 來解釋如何將 DataFrame 的列值的資料型別轉換為字串。

使用 apply() 方法將 DataFrame 的列值的資料型別轉換為字串

import pandas as pd

employees_df = pd.DataFrame({
    'Name': ["Ayush","Bikram","Ceela","Kusal","Shanty"],
    'Score': [31, 38, 33, 39,35],
    'Age':  [33,34,38,45,37],

})
print("DataFrame before Conversion:")
print(employees_df,"\n")
print("Datatype of columns before conversion:")
print(employees_df.dtypes,"\n")

employees_df["Age"]=employees_df["Age"].apply(str)

print("DataFrame after conversion:")
print(employees_df,"\n")
print("Datatype of columns after conversion:")
print(employees_df.dtypes)

輸出:

DataFrame before Conversion:
     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37

Datatype of columns before conversion:
Name     object
Score     int64
Age       int64
dtype: object

DataFrame after conversion:
     Name  Score Age
0   Ayush     31  33
1  Bikram     38  34
2   Ceela     33  38
3   Kusal     39  45
4  Shanty     35  37

Datatype of columns after conversion:
Name     object
Score     int64
Age      object
dtype: object

它將 Age 列的資料型別從 int64 改為代表字串的 object 型別。

使用 applymap() 方法將所有 DataFrame 列的資料型別轉換為 string

如果我們想將 DataFrame 中所有列值的資料型別改為 string 型別,我們可以使用 applymap() 方法。

import pandas as pd

employees_df = pd.DataFrame({
    'Name': ["Ayush","Bikram","Ceela","Kusal","Shanty"],
    'Score': [31, 38, 33, 39,35],
    'Age':  [33,34,38,45,37],

})
print("DataFrame before Conversion:")
print(employees_df,"\n")
print("Datatype of columns before conversion:")
print(employees_df.dtypes,"\n")

employees_df=employees_df.applymap(str)

print("DataFrame after conversion:")
print(employees_df,"\n")
print("Datatype of columns after conversion:")
print(employees_df.dtypes)

輸出:

DataFrame before Conversion:
     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37
zeppy@zeppy-G7-7588:~/test/Week-01/taddaa$ python3 1.py
DataFrame before Conversion:
     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37

Datatype of columns before conversion:
Name     object
Score     int64
Age       int64
dtype: object

DataFrame after conversion:
     Name Score Age
0   Ayush    31  33
1  Bikram    38  34
2   Ceela    33  38
3   Kusal    39  45
4  Shanty    35  37

Datatype of columns after conversion:
Name     object
Score    object
Age      object
dtype: object

它將所有 DataFrame 列的資料型別轉換為 string 型別,在輸出中用 object 表示。

使用 astype() 方法將 DataFrame 列值的資料型別轉換為 string

import pandas as pd

employees_df = pd.DataFrame({
    'Name': ["Ayush","Bikram","Ceela","Kusal","Shanty"],
    'Score': [31, 38, 33, 39,35],
    'Age':  [33,34,38,45,37],

})
print("DataFrame before Conversion:")
print(employees_df,"\n")
print("Datatype of columns before conversion:")
print(employees_df.dtypes,"\n")

employees_df["Score"]=employees_df["Score"].astype(str)

print("DataFrame after conversion:")
print(employees_df,"\n")
print("Datatype of columns after conversion:")
print(employees_df.dtypes)

輸出:

DataFrame before Conversion:
     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37

Datatype of columns before conversion:
Name     object
Score     int64
Age       int64
dtype: object

DataFrame after conversion:
     Name Score  Age
0   Ayush    31   33
1  Bikram    38   34
2   Ceela    33   38
3   Kusal    39   45
4  Shanty    35   37

Datatype of columns after conversion:
Name     object
Score    object
Age       int64
dtype: object

它將 employees_df Dataframe 中 Score 列的資料型別轉換為 string 型別。

Author: Suraj Joshi
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Suraj Joshi is a backend software engineer at Matrice.ai.

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