在这种情况下,设置参数: df.apply(pd.to_numeric, errors='ignore') 然后该函数将被应用于整个DataFrame,可以转换为数字类型的列将被转换,而不能(例如,它们包含非数字字符串或日期)的列将被单独保留。 另外pd.to_datetime和pd.to_timedelta可将数据转换为日期和时间戳。 软转换——类型自动推断 版本0.21.0引入...
will also try to change non-numeric objects (such as strings) into integers or floating-point numbers as appropriate.to_numeric()input can be aSeriesor a column of adataFrame. If some values can’t be converted to a numeric type,to_numeric()allows us to force non-numeric values to ...
代码语言:javascript 代码运行次数:0 运行 复制 In [33]: table = pa.table([pa.array([1, 2, 3], type=pa.int64())], names=["a"]) In [34]: df = table.to_pandas(types_mapper=pd.ArrowDtype) In [35]: df Out[35]: a 0 1 1 2 2 3 In [36]: df.dtypes Out[36]: a int64...
这将有助于将obj转换为float类型。df 'column_name'] = pd.to_numeric（df 'column_name']，errors...
修改单列的数据类型 import pandas as...pd.read_csv('test.csv') df['column_name'] = df['column_name'].astype(np.str) print(df.dtypes) 2.修改指定多列的数据类型...pandas as pd df[['c3','c5']] = df[['c3','c5']].apply(pd.to_numeric) print(df.dtypes) 3.创建dataframe时,...
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 change one or more columns in a Pandas DataFrame into a numeric object. This function convert...
to_timestamp([freq, how, axis, copy])将时间戳的数据类型转换为DatatimeIndex，位于周期的开始处。
apply(pd.to_numeric, errors='coerce').fillna(0) df Trick 8 缩减数据的体积 drinks.info(memory_usage='deep') ## 30.5 KB <class 'pandas.core.frame.DataFrame'> RangeIndex: 193 entries, 0 to 192 Data columns (total 6 columns): # Column Non-Null Count Dtype --- --- --- --- 0 ...
but the column does not get converted. When I use errors = 'raise' it gives me the numbers that are not convertible but it should be dropping them with coerce... This was working perfectly in Pandas 0.19 and i Updated to 0.20.3. Did the way to_numeric works change between the two...
# Change specific column type df.Fee = df['Fee'].astype('int') print(df.dtypes) # Multiple columns integer conversion df[['Fee', 'Discount']] = df[['Fee','Discount']].astype(int) print(df.dtypes) # Convert the strings to integers use to_numeric ...