CODEWITHSUNDEEP
pythonpandashistogram
smd1000
smd1000

Reputation: 313

creating histograms in pandas

I'm trying to create a histogram based on the following groupby,

dfm.groupby(['ID', 'Readings', 'Condition']).size:
578871001  20110603         True    1
           20110701         True    1
           20110803         True    1
           20110901         True    1
           20110930         True    1
                                          ..
324461897  20130214         False            1
           20130318         False            1
           20130416         False            1
           20130516         False            1
           20130617         False            1
532674350  20110616         False            1
           20110718         False            1
           20110818         False            1
           20110916         False            1
           20111017         False            1
           20111115         False            1
           20111219         False            1

However, I'm trying to format the output by Condition and group the number of ID and Readings. Something like this,

True
 # of Readings: # of ID
  1 : 5
  2 : 8
  3 : 15
  4 : 10
  5 : 4

I've tried grouping just by ID and Readings, and transforming by Condition, but have not gotten very far.

Edit:

This is what the dataframe looked like before the groupby:

         CustID     Condtion      Month          Reading  Consumption
0     108000601         True       June         20110606      28320.0
1     108007000         True       July         20110705      13760.0
2     108007000         True     August         20110804      16240.0
3     108008000         True  September         20110901      12560.0
4     108008000         True    October         20111004      12400.0
5     108000601        False   November         20111101       9440.0
6     108090000        False   December         20111205      12160.0

Upvotes: 1

Views: 223

Answers (1)

Alexander
Alexander

Reputation: 109706

Is this what you are trying to achieve with your groupby? I've included Counter to track the count of each reading. For example, for Condtion = False, there are two CustIDs with a single reading, so the output of the first row is:

Condtion
False   1  2  # One reading, two observations of one reading.

Then, for Condtion = True, there is one customer with one reading (108000601) and two customers with two readings each. The output for this group is:

Condtion
True   1  1  # One customer with one reading.
       2  2  # Two customers with two readings each.


from collections import Counter

gb = df.groupby(['Condtion', 'CustID'], as_index=False).Reading.count()
>>> gb
  Condtion     CustID  Reading
0    False  108000601        1
1    False  108090000        1
2     True  108000601        1
3     True  108007000        2
4     True  108008000        2

>>> gb.groupby('Condtion').Reading.apply(lambda group: Counter(group))

Condtion   
False     1    2
True      1    1
          2    2
dtype: float64

Or, chained together as a single statement:

gb = (df
      .groupby(['Condtion', 'CustID'], as_index=False)['Reading']
      .count()
      .groupby('Condtion')['Reading']
      .apply(lambda group: Counter(group))
)

Upvotes: 1

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