# Quick examples of convert string to integer# Example 1: Convert string to an integerdf["Fee"]=df["Fee"].astype(int)print(df.dtypes)# Example 2: Change specific column typedf.Fee=df['Fee'].astype('int')print(df
You can useDataFrame.astype(int)orDataFrame.apply()method to convert a column to int (float/string to integer/int64/int32 dtype) data type. If you are converting float, you would know float is bigger than int type, and converting into int would lose any value after the decimal. Advertisem...
df=pd.DataFrame(Data) # convert string to an integer df['Problems']=pd.to_numeric(df['Problems']) # show the dataframe print(df) print("-"*30) # show the data type # of each column print(df.dtypes) 输出:
# 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) print...
千真万确。如果将列转换为“str”而不是“string”,则结果将是具有可能的“nan”值的对象类型。如果您随后将数据帧保存为 Null 合理格式,例如 Parquet 文件,您将因为这个“str”而感到非常头痛。我花了几个小时才找到问题,`df['column_name'] = df['column_name'].astype("string")`解决了它(2认同)...
Method 2 : Convert float type column to int using astype() method with dictionary Method 3 : Convert float type column to int using astype() method by specifying data types Method 4 : Convert string/object type column to int using astype() method Method 5 : Convert string/object type...
可以看到国家字段是object类型,受欢迎度是int整数类型,评分与向往度都是float浮点数类型。而实际上,对于向往度我们可能需要的是int整数类型,国家字段是string字符串类型。 那么,我们可以在加载数据的时候通过参数dtype指定各字段数据类型。 代码语言:javascript ...
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 ...
The to_numeric() function is used to convert the string values of the Series into appropriate integer values. If you use floating numbers rather than int then column will be converted to float. 1 2 3 4 5 6 import pandas as pd x=pd.Series(['3.5',5.2,'8',4.2,'9']) print(x) ...
to_records([index, column_dtypes, index_dtypes]) 将DataFrame转换为NumPy记录数组。to_sql(name, con[, schema, if_exists, …]) 将存储在DataFrame中的记录写入SQL数据库。to_stata(**kwargs) 将DataFrame对象导出为Stata dta格式。to_string([buf, columns, col_space, header, …]) 将DataFrame渲染到...