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)”协议。
2.3)Pandas列的类型转换(转换为str,int) K总写字的地方 中国注册会计师、具有法律职业资格、期货、银行和证券资格 来自专栏 · python 编程 K同学:Pandas目录3 赞同 · 0 评论文章 省份 城市 区 人口 GDP 气温 地形 气温.1 test 0 北京 北京 崇文 456 1112 1 平原 3 1.0 1 北京 北京 宣武 153 142...
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 as param. To cast to a32-bit ...
转换int类型:data[['m']] = data[['m']].astype(int) 报错:OverflowError: Python int too large to convert to C long 解决:data[['m']] = data[['m']].astype('int64') 即可! 参考文档: https://blog.csdn.net/weixin_43187555/article/details/103995364...
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
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 ...
Different methods to convert column to int in pandas DataFrame Create pandas DataFrame with example data Method 1 : Convert float type column to int using astype() method Method 2 : Convert float type column to int using astype() method with dictionary Method 3 : Convert float type colu...
# 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) ...
df['a_int'] = pd.to_numeric(df['a'], errors='coerce').fillna(0) 红框为转换后数据 所属组数据列中包含一个非数值,用astype()转换会出现错误,然而用to_numeric()函数处理就优雅很多。 3.2to_datetime # 定义转换前数据 df = pd.DataFrame({'month': [5, 5, 5], 'day':[11, 3, 22], ...
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 ...