How to create crosstabs from a Dictionary in Python?

In this article, we are going to see how to create crosstabs from dictionaries in Python. The pandas crosstab function builds a cross-tabulation table that can show the frequency with which certain groups of data appear. 

This method is used to compute a simple cross-tabulation of two (or more) factors. By default, computes a frequency table of the factors unless an array of values and an aggregation function are passed.

Syntax: pandas.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name=’All’, dropna=True, normalize=False)

Arguments :

• index : array-like, Series, or list of arrays/Series, Values to group by in the rows.
• columns : array-like, Series, or list of arrays/Series, Values to group by in the columns.
• values : array-like, optional, array of values to aggregate according to the factors. Requires `aggfunc` be specified.
• rownames : sequence, default None, If passed, must match number of row arrays passed.
• colnames : sequence, default None, If passed, must match number of column arrays passed.
• aggfunc : function, optional, If specified, requires `values` be specified as well.
• margins : bool, default False, Add row/column margins (subtotals).
• margins_name : str, default ‘All’, Name of the row/column that will contain the totals when margins is True.
• dropna : bool, default True, Do not include columns whose entries are all NaN.

Stepwise implementation:

 Step 1: Create a dictionary.

raw_data = {'Digimon': ['Kuramon', 'Pabumon', 'Punimon',
                        'Botamon', 'Poyomon', 'Koromon', 
                        'Tanemon', 'Tsunomon', 'Tsumemon', 
                        'Tokomon'],
            'Stage': ['Baby', 'Baby', 'Baby', 'Baby', 'Baby',
                      'In-Training', 'In-Training', 'In-Training',
                      'In-Training', 'In-Training'],
            'Type': ['Free', 'Free', 'Free', 'Free', 'Free', 'Free',
                     'Free', 'Free', 'Free', 'Free'],
            'Attribute': ['Neutral', 'Neutral', 'Neutral',
                          'Neutral', 'Neutral', 'Fire', 'Plant',
                          'Earth', 'Dark', 'Neutral'],
            
            'Memory': [2, 2, 2, 2, 2, 3, 3, 3, 3, 3],
            'Equip Slots': [0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
            'Lv 50 HP': [324, 424, 5343, 52, 63, 42,
                         643, 526, 42, 75],
            'Lv50 SP': [86, 75, 64, 43, 86, 64, 344,
                        24, 24, 12],
            'Lv50 Atk': [86, 74, 6335, 421, 23, 36436, 
                         65, 75, 86, 52]}
print(raw_data)

Output:

{'Digimon': ['Kuramon', 'Pabumon', 'Punimon', 'Botamon', 'Poyomon', 'Koromon', 'Tanemon', 'Tsunomon', 'Tsumemon', 'Tokomon'], 'Stage': ['Baby', 'Baby', 'Baby', 'Baby', 'Baby', 'In-Training', 'In-Training', 'In-Training', 'In-Training', 'In-Training'], 'Type': ['Free', 'Free', 'Free', 'Free', 'Free', 'Free', 'Free', 'Free', 'Free', 'Free'], 'Attribute': ['Neutral', 'Neutral', 'Neutral', 'Neutral', 'Neutral', 'Fire', 'Plant', 'Earth', 'Dark', 'Neutral'], 'Memory': [2, 2, 2, 2, 2, 3, 3, 3, 3, 3], 'Equip Slots': [0, 0, 1, 1, 1, 1, 1, 1, 1, 1], 'Lv 50 HP': [324, 424, 5343, 52, 63, 42, 643, 526, 42, 75], 'Lv50 SP': [86, 75, 64, 43, 86, 64, 344, 24, 24, 12], 'Lv50 Atk': [86, 74, 6335, 421, 23, 36436, 65, 75, 86, 52]} 

Step 2: Create a dataframe by using the Pandas Dataframe function.

import pandas as pd
raw_data_df = pd.DataFrame(raw_data,columns= ['Digimon','Stage',
                                            'Type', 'Attribute',
                                            'Memory','Equip Slots',
                                            'Lv 50 HP','Lv50 SP',
                                            'Lv50 Atk'])
print(raw_data_df)

Output: 

Step 3: Using crosstab.

import pandas as pd
raw_data_df=pd.DataFrame(raw_data,columns= ['Digimon','Stage',
                                            'Type',
                                            'Attribute','Memory',
                                            'Equip Slots',
                                            'Lv 50 HP','Lv50 SP',
                                            'Lv50 Atk'])
print(raw_data_df)

Output:

You can add multiple indices (rows) to a crosstab as well. This can be done by passing a list of variables to the crosstab function, you wanted to break items down by region and quarter, you can pass these into the index parameter.

raw_data_fd = pd.crosstab(
    [raw_data_df['Attribute'], raw_data_df['Memory']],
  raw_data_df['Digimon'], margins=True)
raw_data_fd

Output