Pandas 将列值转换为字符串
Suraj Joshi
2023年1月30日
2021年1月22日
-
使用
apply()方法将 DataFrame 的列值的数据类型转换为字符串 -
使用
applymap()方法将所有 DataFrame 列的数据类型转换为string -
使用
astype()方法将 DataFrame 列值的数据类型转换为string
本教程介绍了如何将 DataFrame 的列值的数据类型转换为字符串。
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)
输出：
Name Score Age
0 Ayush 31 33
1 Bikram 38 34
2 Ceela 33 38
3 Kusal 39 45
4 Shanty 35 37
我们将使用上面例子中显示的 DataFrame 来解释如何将 DataFrame 的列值的数据类型转换为字符串。
使用 apply() 方法将 DataFrame 的列值的数据类型转换为字符串
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)
输出：
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
它将 Age 列的数据类型从 int64 改为代表字符串的 object 类型。
使用 applymap() 方法将所有 DataFrame 列的数据类型转换为 string
如果我们想将 DataFrame 中所有列值的数据类型改为 string 类型，我们可以使用 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)
输出：
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
它将所有 DataFrame 列的数据类型转换为 string 类型，在输出中用 object 表示。
使用 astype() 方法将 DataFrame 列值的数据类型转换为 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)
输出：
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
它将 employees_df Dataframe 中 Score 列的数据类型转换为 string 类型。
Author: Suraj Joshi
Suraj Joshi is a backend software engineer at Matrice.ai.
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