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Pandas: Split a given dataset, group by two columns and convert other columns of the dataframe into a dictionary with column header as key

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-26 with Solution

Write a Pandas program to split a given dataset, group by two columns and convert other columns of the dataframe into a dictionary with column header as key.

Test Data:

   school class            name date_Of_Birth   age  height   weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4

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({
    'school_code': ['s001','s002','s003','s001','s002','s004'],
    'class': ['V', 'V', 'VI', 'VI', 'V', 'VI'],
    'name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Gino Mcneill', 'David Parkes'],
    'date_Of_Birth ': ['15/05/2002','17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],
    'age': [12, 12, 13, 13, 14, 12],
    'height': [173, 192, 186, 167, 151, 159],
    'weight': [35, 32, 33, 30, 31, 32],
    'address': ['street1', 'street2', 'street3', 'street1', 'street2', 'street4']},
    index=['S1', 'S2', 'S3', 'S4', 'S5', 'S6'])
print("Original DataFrame:")
print(df)
dict_data_list = list()

for gg, dd in df.groupby(['school_code','class']):
    group = dict(zip(['school_code','class'], gg))
    ocolumns_list = list()
    for _, data in dd.iterrows():
        data = data.drop(labels=['school_code','class'])
        ocolumns_list.append(data.to_dict())
    group['other_columns'] = ocolumns_list
    dict_data_list.append(group)

print(dict_data_list) 

Sample Output:

Original DataFrame:
   school_code class            name date_Of_Birth   age  height  weight  \
S1        s001     V  Alberto Franco     15/05/2002   12     173      35   
S2        s002     V    Gino Mcneill     17/05/2002   12     192      32   
S3        s003    VI     Ryan Parkes     16/02/1999   13     186      33   
S4        s001    VI    Eesha Hinton     25/09/1998   13     167      30   
S5        s002     V    Gino Mcneill     11/05/2002   14     151      31   
S6        s004    VI    David Parkes     15/09/1997   12     159      32   

    address  
S1  street1  
S2  street2  
S3  street3  
S4  street1  
S5  street2  
S6  street4  
[{'school_code': 's001', 'class': 'V', 'other_columns': [{'name': 'Alberto Franco', 'date_Of_Birth ': '15/05/2002', 'age': 12, 'height': 173, 'weight': 35, 'address': 'street1'}]}, 
{'school_code': 's001', 'class': 'VI', 'other_columns': [{'name': 'Eesha Hinton', 'date_Of_Birth ': '25/09/1998', 'age': 13, 'height': 167, 'weight': 30, 'address': 'street1'}]},
{'school_code': 's002', 'class': 'V', 'other_columns': [{'name': 'Gino Mcneill', 'date_Of_Birth ': '17/05/2002', 'age': 12, 'height': 192, 'weight': 32, 'address': 'street2'}, {'name': 'Gino Mcneill', 'date_Of_Birth ': '11/05/2002', 'age': 14, 'height': 151, 'weight': 31, 'address': 'street2'}]},
{'school_code': 's003', 'class': 'VI', 'other_columns': [{'name': 'Ryan Parkes', 'date_Of_Birth ': '16/02/1999', 'age': 13, 'height': 186, 'weight': 33, 'address': 'street3'}]},
{'school_code': 's004', 'class': 'VI', 'other_columns': [{'name': 'David Parkes', 'date_Of_Birth ': '15/09/1997', 'age': 12, 'height': 159, 'weight': 32, 'address': 'street4'}]}]

Python Code Editor:


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Previous: Write a Pandas program to split a dataset, group by one column and get mean, min, and max values by group, also change the column name of the aggregated metric. Using the following dataset find the mean, min, and max values of purchase amount (purch_amt) group by customer id (customer_id).
Next: Write a Pandas program to split a given dataset, group by one column and apply an aggregate function to few columns and another aggregate function to the rest of the columns of the dataframe.

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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