how to select the data of a Dataframe (Python)

• ktflash

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  March 4, 2022 at 12:55 pm

  #3993042

  ahoi,

  i have gotta admit i struggle a bit with the querying of data from hierarchical indexed dataframes.

  Heres the dataframe creation.

  import pandas as pd
  import numpy as np
  
  multi_indexed_DF = pd.DataFrame(np.random.rand(4, 2),
  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
  columns=['data1', 'data2'],)

  How do i comebine the first 2 selection methods (filter_1, filter_2) to get a propery combination of both filters:

  # get only columns data1,data2 from first index with 'a', 'b' and for each one only the 2nd index val=1
  filter_1 = multi_indexed_DF.loc[(['a','b'], 1), ['data1','data2']]
  
  # get only those entries where data1 is more than 0.5 (random numbers so the result varies)
  filter_2 = multi_indexed_DF.loc[multi_indexed_DF.data1 > 0.5]
  
  # so my question is how do i add the 2nd filter of data1 > 0.5 on on top of the first selection
  # the full dataframe multi_indexed_DF, filter_1, filter_2
  # this one seems to work, but the system tells me not to use it because its depricated and will be gone 
  # so i am looking for the "proper" way(s) toactually write this 
  filter_3 = multi_indexed_DF[multi_indexed_DF.data1 > 0.5].loc[(['a','b'], 1), ['data1','data2']]
  
  """
  # here is the result example from an execution with random numbers of mine for
  # FULL
          data1     data2
  a 1  0.295029  0.815447
    2  0.481834  0.529683
  b 1  0.981762  0.021112
    2  0.995068  0.579253 
  
  # Filter 1
          data1     data2
  a 1  0.295029  0.815447
  b 1  0.981762  0.021112 
  
  # Filter 2
          data1     data2
  b 1  0.981762  0.021112
    2  0.995068  0.579253
  
  # Execpted Result for combined Filter 3: result of filter 1 without index a 1 because a data1 > 0.5
          data1     data2
  b 1  0.981762  0.021112 
  """
  

  Thanks

   

   

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• Site Owners

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  March 5, 2022 at 1:10 pm

  #3993941

  Thanks for posting your issue and hopefully someone will answer soon.

  This is an automated bump to increase visibility of your question.

• April 25, 2022 at 2:03 pm

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• May 9, 2022 at 10:04 am

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