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Pandas: Split a given dataframe into groups with bin counts

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-18 with Solution

Write a Pandas program to split a given dataframe into groups with bin counts.

Test Data:

    ord_no  purch_amt  customer_id  sales_id
0    70001     150.50         3005      5002
1    70009     270.65         3001      5003
2    70002      65.26         3002      5004
3    70004     110.50         3009      5003
4    70007     948.50         3005      5002
5    70005    2400.60         3007      5001
6    70008    5760.00         3002      5005
7    70010    1983.43         3004      5007
8    70003    2480.40         3009      5008
9    70012     250.45         3008      5004
10   70011      75.29         3003      5005
11   70013    3045.60         3002      5001

Sample Solution:

Python Code :

import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
df = pd.DataFrame({
'ord_no':[70001,70009,70002,70004,70007,70005,70008,70010,70003,70012,70011,70013],
'purch_amt':[150.5,270.65,65.26,110.5,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,3045.6],
'customer_id':[3005,3001,3002,3009,3005,3007,3002,3004,3009,3008,3003,3002],
'sales_id':[5002,5003,5004,5003,5002,5001,5005,5007,5008,5004,5005,5001]})
print("Original DataFrame:")
print(df)
groups = df.groupby(['customer_id', pd.cut(df.sales_id, 3)])
result = groups.size().unstack()
print(result)

Sample Output:

Original DataFrame:
    ord_no  purch_amt  customer_id  sales_id
0    70001     150.50         3005      5002
1    70009     270.65         3001      5003
2    70002      65.26         3002      5004
3    70004     110.50         3009      5003
4    70007     948.50         3005      5002
5    70005    2400.60         3007      5001
6    70008    5760.00         3002      5005
7    70010    1983.43         3004      5007
8    70003    2480.40         3009      5008
9    70012     250.45         3008      5004
10   70011      75.29         3003      5005
11   70013    3045.60         3002      5001
sales_id     (5000.993, 5003.333]  (5003.333, 5005.667]  (5005.667, 5008.0]
customer_id                                                                
3001                          1.0                   NaN                 NaN
3002                          1.0                   2.0                 NaN
3003                          NaN                   1.0                 NaN
3004                          NaN                   NaN                 1.0
3005                          2.0                   NaN                 NaN
3007                          1.0                   NaN                 NaN
3008                          NaN                   1.0                 NaN
3009                          1.0                   NaN

Python Code Editor:


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Previous: Write a Pandas program to split a given dataframe into groups and create a new column with count from GroupBy.

Next: Write a Pandas program to split a given dataframe into groups with multiple aggregations.

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Python: Tips of the Day

Understanding slice notation:

It's pretty simple really:

a[start:stop]  # items start through stop-1
a[start:]      # items start through the rest of the array
a[:stop]       # items from the beginning through stop-1
a[:]           # a copy of the whole array

There is also the step value, which can be used with any of the above:

a[start:stop:step] # start through not past stop, by step

The key point to remember is that the :stop value represents the first value that is not in the selected slice. So, the difference between stop and start is the number of elements selected (if step is 1, the default).

The other feature is that start or stop may be a negative number, which means it counts from the end of the array instead of the beginning. So:

a[-1]    # last item in the array
a[-2:]   # last two items in the array
a[:-2]   # everything except the last two items

Similarly, step may be a negative number:

a[::-1]    # all items in the array, reversed
a[1::-1]   # the first two items, reversed
a[:-3:-1]  # the last two items, reversed
a[-3::-1]  # everything except the last two items, reversed

Python is kind to the programmer if there are fewer items than you ask for. For example, if you ask for a[:-2] and a only contains one element, you get an empty list instead of an error. Sometimes you would prefer the error, so you have to be aware that this may happen.

Relation to slice() object

The slicing operator [] is actually being used in the above code with a slice() object using the : notation (which is only valid within []), i.e.:

a[start:stop:step]

is equivalent to:

a[slice(start, stop, step)]

Slice objects also behave slightly differently depending on the number of arguments, similarly to range(), i.e. both slice(stop) and slice(start, stop[, step]) are supported. To skip specifying a given argument, one might use None, so that e.g. a[start:] is equivalent to a[slice(start, None)] or a[::-1] is equivalent to a[slice(None, None, -1)].

While the : -based notation is very helpful for simple slicing, the explicit use of slice() objects simplifies the programmatic generation of slicing.

Ref: https://bit.ly/2MHaTp7