pandas

问题 I have (in my opinion) a strange problem with python pandas. If I do: cc1 = cc.copy(deep=True) for the dataframe cc and than ask a certain row and column: print(cc1.loc['myindex']['data'] is cc.loc['myindex']['data']) I get True What's wrong here? 回答1: Deep copying doesn't work in pandas and the devs consider putting mutable objects inside a DataFrame as an antipattern There is nothing wrong in your code, just in case if you want to know the difference with some example of deep and shallow

问题 How to do a cumulative concatenate in pandas dataframe? I found there are a number of solutions in R, but can't find it in python. Here is the problem: suppose we have a dataframe: with columns: date and name : import pandas as pd d = {'date': [1,1,2,2,3,3,3,4,4,4], 'name':['A','B','A','C','A','B','B','A','B','C']} df = pd.DataFrame(data=d) I want to get CUM_CONCAT , which is a cumulative concatenate groupby date: date name CUM_CONCAT 0 1 A [A] 1 1 B [A,B] 2 2 A [A] 3 2 C [A,C] 4 3 A [A] 5 3

问题 How to do a cumulative concatenate in pandas dataframe? I found there are a number of solutions in R, but can't find it in python. Here is the problem: suppose we have a dataframe: with columns: date and name : import pandas as pd d = {'date': [1,1,2,2,3,3,3,4,4,4], 'name':['A','B','A','C','A','B','B','A','B','C']} df = pd.DataFrame(data=d) I want to get CUM_CONCAT , which is a cumulative concatenate groupby date: date name CUM_CONCAT 0 1 A [A] 1 1 B [A,B] 2 2 A [A] 3 2 C [A,C] 4 3 A [A] 5 3

问题 I have got 3 data frames: energy, GDP & ScimEn. All the data frames have a column 'Country' and I merged all 3 data frames while using inner join: a = pd.merge(energy,GDP,left_on='Country',right_on='Country',how='inner') b = pd.merge(a,ScimEn,left_on='Country',right_on='Country',how='inner') Now, I want to figure out the number of countries which were left out of this merge. I tried the following formula, but it's giving me an error "ValueError: Cannot use name of an existing column for

问题 I have got 3 data frames: energy, GDP & ScimEn. All the data frames have a column 'Country' and I merged all 3 data frames while using inner join: a = pd.merge(energy,GDP,left_on='Country',right_on='Country',how='inner') b = pd.merge(a,ScimEn,left_on='Country',right_on='Country',how='inner') Now, I want to figure out the number of countries which were left out of this merge. I tried the following formula, but it's giving me an error "ValueError: Cannot use name of an existing column for

问题 This is somewhat of a broad topic, but I will try to pare it to some specific questions. I have noticed a difference between resample and groupby that I am curious to learn about. Here is some hourly time series data: In[]: import pandas as pd dr = pd.date_range('01-01-2020 8:00', periods=10, freq='H') df = pd.DataFrame({'A':range(10), 'B':range(10,20), 'C':range(20,30)}, index=dr) df Out[]: A B C 2020-01-01 08:00:00 0 10 20 2020-01-01 09:00:00 1 11 21 2020-01-01 10:00:00 2 12 22 2020-01-01

问题 This is somewhat of a broad topic, but I will try to pare it to some specific questions. I have noticed a difference between resample and groupby that I am curious to learn about. Here is some hourly time series data: In[]: import pandas as pd dr = pd.date_range('01-01-2020 8:00', periods=10, freq='H') df = pd.DataFrame({'A':range(10), 'B':range(10,20), 'C':range(20,30)}, index=dr) df Out[]: A B C 2020-01-01 08:00:00 0 10 20 2020-01-01 09:00:00 1 11 21 2020-01-01 10:00:00 2 12 22 2020-01-01

问题 This is somewhat of a broad topic, but I will try to pare it to some specific questions. I have noticed a difference between resample and groupby that I am curious to learn about. Here is some hourly time series data: In[]: import pandas as pd dr = pd.date_range('01-01-2020 8:00', periods=10, freq='H') df = pd.DataFrame({'A':range(10), 'B':range(10,20), 'C':range(20,30)}, index=dr) df Out[]: A B C 2020-01-01 08:00:00 0 10 20 2020-01-01 09:00:00 1 11 21 2020-01-01 10:00:00 2 12 22 2020-01-01

问题 I've got a Series of numpy arrays: import pandas as pd import numpy as np pd.Series({10: np.array([[0.72260683, 0.27739317, 0. ], [0.7187053 , 0.2812947 , 0. ], [0.71435467, 0.28564533, 1. ], [0.3268072 , 0.6731928 , 0. ], [0.31941951, 0.68058049, 1. ], [0.31260015, 0.68739985, 0. ]]), 20: np.array([[0.7022099 , 0.2977901 , 0. ], [0.6983866 , 0.3016134 , 0. ], [0.69411673, 0.30588327, 1. ], [0.33857735, 0.66142265, 0. ], [0.33244109, 0.66755891, 1. ], [0.32675582, 0.67324418, 0. ]]), 38: np

问题 I have a csv file with data as below XPATH,ColumName,CSV_File_Name,ParentKey /integration-outbound:IntegrationEntity/integrationEntityDetails/supplier/forms/form[]/id,id,integrationEntityDetailsForms.csv, /integration-outbound:IntegrationEntity/integrationEntityHeader/attachments/attachment[]/id,aid,integrationEntityDetailsForms.csv, /integration-outbound:IntegrationEntity/integrationEntityDetails/supplier/forms/form[]/records/record[]/Internalid,Internalid,integrationEntityDetailsForms.csv,