使用astype()方法将选定列的数据类型转换为int: 你可以使用astype()方法将选定列的数据类型转换为int。但是,在转换过程中可能会遇到无法转换为int的数据(如NaN值或字符串数据)。为了处理这些潜在的错误,你可以使用errors='coerce'参数,这将无法转换的值设置为NaN。 python df['your_column
Convert Column to Int (Integer) You can use pandasDataFrame.astype()function to convert column to int(integer). You can apply this to a specific column or to an entire DataFrame. To cast the data type to a 64-bit signed integer, you can use numpy.int64, numpy.int_, int64, or int a...
convert_dict={'A':int, 'C':float} df=df.astype(convert_dict) print(df.dtypes) 输出: 注:本文由VeryToolz翻译自Convert the data type of Pandas column to int,非经特殊声明,文中代码和图片版权归原作者vaishalianand1276所有,本译文的传播和使用请遵循“署名-相同方式共享 4.0 国际 (CC BY-SA 4.0)...
To implement all the methods in this article, we will have to import the Pandas package. Use the to_numeric() function to convert column to int The simplest and the most basic way to convert the elements in a Pandas Series or DataFrame to int. The to_numeric() function is used to ...
column is the float type column to be converted to integer Example: Python program to convert cost column to int python # import the module import pandas # consider the food data food_input={'id':['foo-23','foo-13','foo-02','foo-31'], 'name':['ground-nut oil','almonds','flour...
To convert a string column to an integer in a Pandas DataFrame, you can use the astype() method. To convert String to Int (Integer) from Pandas DataFrame
Pandas DataFrame Exercises, Practice and Solution: Write a Pandas program to convert the datatype of a given column(floats to ints).
数值类型包括int和float。 转换数据类型比较通用的方法可以用astype进行转换。 pandas中有种非常便利的方法to_numeric()可以将其它数据类型转换为数值类型。 pandas.to_numeric(arg, errors='raise', downcast=None) arg:被转换的变量,格式可以是list,tuple,1-d array,Series ...
# import pandas libraryimportpandasaspd# dictionaryData = {'Name':['GeeksForGeeks','Python'],'Unique ID':['900','450']}# create a dataframe objectdf = pd.DataFrame(Data)# convert string to an integerdf['Unique ID'] = df['Unique ID'].astype(int)# show the dataframeprint(df) ...
RangeIndex: 4 entries, 0 to 3 Data columns (total 8 columns): # Column Non-Null Count Dtype --- --- --- --- 0 string_col 4 non-null object 1 int_col 4 non-null int64 2 float_col 4 non-null float64 3 mix_col 4 non-null ...