20,30],'C':['pandasdataframe.com','modify','columns']})# 定义一个函数,如果数值大于10,加10defadd_ten(x):returnx+10ifx>10elsex# 对'A'和'B'列应用条件函数df[['A','B']]=df[['A','B']].applymap(add_ten)print(df)
可以对 DataFrame 的多列使用apply函数,并传递多个参数。 importpandasaspd# 创建DataFramedf=pd.DataFrame({'A':range(1,6),'B':range(10,15)})# 定义一个处理多列的函数defsum_columns(x,y,factor):return(x+y)*factor# 使用 apply 函数df['C']=df.apply(lambdarow:sum_columns(row['A'],row['B...
df.info() # 查看索引、数据类型和内存信息 df.columns() # 查看字段()名称 df.describe() # 查看汇总统计 s.value_counts() # 统计某个值出现次数 df.apply(pd.Series.value_counts) # 查看DataFrame对象中每列的唯值和计数 df.isnull().any() # 查看是否有缺失值 df[df[column_name].duplicated()...
# return a dataframe object grouped by "species" column df.groupby("species") After the dataframe...groupby "species" categories df["sepal_length"].groupby(df["species"]).mean() Or you can apply such...aggregate function to multiple features: # group each column by "species", then ...
6 rows x 16 columns] Another aggregation example is to compute the number of unique values of each group. This is similar to thevalue_countsfunction, except that it only counts unique values. In [77]: ll = [['foo', 1], ['foo', 2], ['foo', 2], ['bar', 1], ['bar', 1]...
Function03 concat(objs: 'Iterable[NDFrame] | Mapping[Hashable, NDFrame]', axis=0, join='outer', ignore_index: 'bool' = False, keys=None, levels=None, names=None, verify_integrity: 'bool' = False, sort: 'bool' = False, copy: 'bool' = True) -> 'FrameOrSeriesUnion' ...
# 自定义一个求SAT数学成绩的加权平均值的函数 In[76]:defweighted_math_average(df):weighted_math=df['UGDS']*df['SATMTMID']returnint(weighted_math.sum()/df['UGDS'].sum())# 按州分组,并调用apply方法,传入自定义函数 In[77]:college2.groupby('STABBR').apply(weighted_math_average).head(...
一、前言二、本文概要三、pandas merge by 修罗闪空3.1 merge函数用途3.2 merge函数的具体参数3.3 merge函数的应用四、pandas apply by pluto、乔瞧4.1 pandas apply by pluto4.2 pandas apply by 乔瞧pandas pivot_table by 石墨锡 一、前言 本文来自四位读者的合作,这四位读者是之前推文14个pandas神操作,手把手...
df.index|columns = df.index|columns.map(Function) #对索引值使用函数进行转换 二、离散数据分组 1、普通分组:cut cats = pd.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False) Return indices of half-open bins to which each value of `x` belongs. x:必...
The underlying Python function takes an iterator of a tuple of pandas Series. The wrapped pandas UDF takes multiple Spark columns as an input. You specify the type hints as Iterator[Tuple[pandas.Series, ...]] -> Iterator[pandas.Series]. Python Copy from typing import Iterator, Tuple import...