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fix unique bin error:duplicates='drop'decrease quantilesRank your data with DataFrame.rank(method=‘first’). The ranking assigns a unique value to each element in the dataframe (the rank) while keeping the order of the elements (except for identica

ValueError: Incompatible indexer with Seriesreason: This problem occurs when a key in the dict refers to more than one value!e.g.:df = pd.DataFrame({"A": [1, 2, 3]})df# A#0 1#1 2#2 3num = np.mean(df)num#A 2.0#dtype: float64Then, when usi.

e.g. idx = '2020-03-20'n = 20# n rows beforedf.loc[:idx].tail(n)# n rows afterdf.loc[idx:].head(n)source: Get number of rows before and after a certain index value in pandas

一个datetime类型的日期 cur_dt = '2021-03-01'想要得到前一天、后一天的日期：import datetimeprevious_date = cur_dt - datetime.timedelta(1)# output: Timestamp('2021-02-28 00:00:00')next_date = cur_dt + datetime.timedelta(1)# output: Timestamp('2021-03-02 00:00:00')...

检查两列index是否一样，如不一样：df1 = df1.reset_index()df2 = df2.reset_index()df1['column1'] / df2['column1']如需例子可看：stackoverflow: Pandas divide creates extra columns and NaN

先尝试了：df.index是date，类型是datetimedf[df.index.day == df.index.days_in_month]和df[df.index == (df.index + pd.offsets.MonthEnd(0))]会只输出月底最后一天是交易日的数据如下方法成功：df.loc[df.groupby(df.index.to_period('M')).apply(lambda x: x.index.max())]两种方法来源：https://stackov

list1 = []#method 1if not len(list1): print('empty')else: print('not empty')#method 2if not list1: print('empty')else: print('not empty')

列名重命名多种方法df.columns = list(range(5))df.rename(columns = {'original':'now'}, inplace = True)

在对dataframe中的一列做log变换时试图使用math.log(df['col1'])，提示错误：TypeError: cannot convert the series to <class ‘float’>正确方法：df['col1'].apply(np.log)方法来源于：Pandas入门：对一列取指数/对数...

根据列名获取列的位置：df.get_loc('name')根据列的位置获取列名df[df.columns[position]]

total[col].value_counts()Result:total = pd.merge(df1, df2)

pandas索引``data = {‘Team’: [‘Riders’, ‘Riders’, ‘Devils’, ‘Devils’, ‘Kings’,‘kings’, ‘Kings’, ‘Kings’, ‘Riders’, ‘Royals’, ‘Royals’, ‘Riders’],‘Rank’: [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],‘Year’: [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,20

注意之前设定的数据类型df['num'] = 0df['num'][i] = 0.1*0.3output = 0df['num'] = float(0)df['num'][i] = 0.1*0.3output = 0.03