defconvert_float_to_string(df):# 遍历DataFrame的每一列forcolumnindf.columns:# 判断列的数据类型是否为floatifdf[column].dtype=='float64':# 转换并去掉小数点df[column]=df[column].astype(str).str.replace('.','')returndf# 使用函数并查看结果df_converted=convert_float_to_string(df)print("转换...
print(df.to_string(col_space=10)) 1. 自定义精度 如果你的数据中有浮点数,也可以通过float_format参数来指定小数点后几位的精度,这在准备数据报告时非常有用。示例如下: data_float={'分数':[88.5,92.3,79.5],}df_float=pd.DataFrame(data_float)print(df_float.to_string(float_format='%.1f')) 1....
col("floats_as_string").cast(pl.Float64), ) print(out) df = pl.DataFrame( { "integers": [-1, 0, 2, 3, 4], "floats": [0.0, 1.0, 2.0, 3.0, 4.0], "bools": [True, False, True, False, True], } ) out = df.select(pl.col("integers").cast(pl.Boolean), pl.col("...
columns为列名,表格内的具体参数值为values importpandasaspdimportnumpyasnpdf=pd.DataFrame({'A':1.,'B':pd.Timestamp('20130102'),'C':pd.Series(1,index=list(range(4)),dtype='float32'),'D':np.array([3]*4,dtype='int32'),'E':pd.Categorical(['test','train','test','train']),'F'...
DataFrame - to_string() function The to_string() function is used to render a DataFrame to a console-friendly tabular output. Syntax: DataFrame.to_string(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=No...
obj2 = pd.Series([4, 7, -5, 3], index=["d", "b", "a", "c"], dtype="float") pandas中常见的数据类型如下: #给series起个名称 obj2 = pd.Series([4, 7, -5, 3], index=["d", "b", "a", "c"], dtype="string", name="sdata") ...
sepalwidth float64 petallength float64 petalwidth float64 name string null_col1 string# 无法识别,通过unknown_as_string设置成STRING类型。null_col2 float64# 强制转换成FLOAT类型。} 示例2:指定list_col列类型为list<int64>。 df4 = DataFrame(df3, as_type={'list_col':'list<int64>'})print(df4...
(data, dtype='float') print(df.dtypes) # method2...df = pd.DataFrame(data, dtype=np.float64) print(df.dtypes) 4.读取时,修改数据类型 import pandas as pd df = pd.read_csv...("somefile.csv", dtype = {'column_name' : str}) df = pd.DataFrame(data, dtype='float') df = pd...
['2016','2017','2018','2019'],'Inflation Rate':['4.47','5','5.98','4.1']}# create a dataframedf=pd.DataFrame(Data)# converting each value# of column to a stringdf['Inflation Rate']=df['Inflation Rate'].astype(float)# show the dataframeprint(df)# show the datatypesprint(df....
如果您需要调用MaxCompute上的内建或者已定义函数来生成列,您可以使用func接口,该接口默认函数返回值为STRING,可以用rtype参数指定返回值。 >>> from odps.df import func >>> >>> iris[iris.name, func.rand(rtype='float').rename('rand')][:4] >>> iris[iris.name, func.rand(10, rtype='float...