df['string_col'] = df['string_col'].astype('int') 当然我们从节省内存的角度上来考虑,转换成int32或者int16类型的数据, df['string_col'] = df['string_col'].astype('int8') df['string_col'] = df['string_col'].astype('int16') df['string_col'] = df['string_col'].astype('int3...
虽然Series类似于 ndarray,如果你需要一个实际的ndarray,那么请使用Series.to_numpy()。 代码语言:javascript 代码运行次数:0 运行 复制 In [20]: s.to_numpy() Out[20]: array([ 0.4691, -0.2829, -1.5091, -1.1356, 1.2121]) 即使Series由ExtensionArray支持,Series.to_numpy()将返回一个 NumPy ndarray。
If you are in a hurry, below are some quick examples of how to convert string to float. You can apply these toconvert from any type in Pandas. # Quick examples of converting string to float# Example 1: Convert "Fee" from string to floatdf['Fee']=df['Fee'].astype(float)print(df....
)) '' description := { "index": Int64Col(shape=(), dflt=0, pos=0), "values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1), "B": Float64Col(shape=(), dflt=0.0, pos=2)} byteorder := '
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渲染到控制台友好的表格输出。to_timestamp([freq, how, axis, copy]) 在时段开始时将其...
comment=None,skip_footer=0,skipfooter=0,convert_float=True,mangle_dupe_cols=True,**kwds) 参数说明: io:文件路径 io = r’D:\test.xlsx’ sheet_name:表名,可指定读取单表、多表、全部表 sheet_name =None# 读取全部表,得到 OrderDict:key为表名,value为 DataFramesheet_name =1/ “Sheet1”# 读...
importnumpy as npimportpandas as pd#从csv文件读取数据,数据表格中只有5行,里面包含了float,string,int三种数据python类型,也就是分别对应的pandas的float64,object,int64df = pd.read_csv("sales_data_types.csv", index_col=0)print(df) Customer Number Customer Name 2016 2017 \ ...
defconvert_currency(val):"""Convert the string number value to a float - Remove $ - Remove commas - Convert to float type"""new_val= val.replace(',','').replace('$','')returnfloat(new_val) df['2016']=df['2016'].apply(convert_currency) ...
to_string()用于返回 DataFrame 类型的数据,我们也可以直接处理 JSON 字符串。 实例 importpandasaspd data=[ { "id":"A001", "name":"菜鸟教程", "url":"www.runoob.com", "likes":61 }, { "id":"A002", "name":"Google", "url":"www.google.com", ...
Parameters --- df: pd.DataFrame Data frame to convert. deep_copy: bool Whether or not to perform a deep copy of the original data frame. Returns --- pd.DataFrame Optimized copy of the input data frame. """ return df.copy(deep=deep_copy).astype({ col: 'category' for col in df....