Crosstab in Python Pandas

In this tutorial, we will learn about Crosstab in Python Pandas. Also, we have covered these topics.

• Crosstab in Python Pandas
• Crosstab in Python Pandas Example
• Ccrosstab pandas dataframe
• Crosstab pandas sum
• Crosstab pandas normalize
• Crosstab pandas normalize percentage
• Crosstab pandas plot
• Crosstab pandas values
• Crosstab pandas count
• Crosstab pandas aggfunc
• Pandas crosstab values cannot be used without an aggfunc
• Pandas crosstab sort values
• Pandas crosstab missing values
• Pandas crosstab null values
• Pandas crosstab count unique values
• Pandas crosstab fill values
• Pandas crosstab default value

Crosstab in Python Pandas

• 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.
• In Python, a crosstab is a tabulation of two different categorical variables.

Syntax:

pandas.crosstab(parameters)

Parameters:

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 a number of row arrays passed.
colnames	sequence, default None If passed, must match a 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).
margin_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.
normalize	bool, {‘all’, ‘index’, ‘columns’}, or {0,1}, default False Normalize by dividing all values by the sum of values.

You may like Groupby in Python Pandas.

Crosstab pandas example

• In this section, we will demonstrate the working of crosstab using the ‘Indian_food’ dataset. We have obtained this dataset from kaggle.
• In this dataset, Indian cuisine consists of a variety of regional and traditional cuisines native to the Indian subcontinent are displayed.
• In the below diagram, we have taken an example of the Assam. In Assam, there are 10 non-vegetarian dishes and 11 vegetarian dishes. So this frequency matrix can be created using the crosstab function in pandas.

Implementation on jupyter notebook.

Crosstab pandas dataframe

• In this section, we will perform the crosstab function after creating DataFrame from scratch.
• DataFrame will be similar to the dataset being used in this tutorial i.e indian_food.csv

Implementation on jupyter notebook:

Crosstab pandas sum

• Sum function is used to add the values of crosstab
• .sum() and .sum both give different results

Crosstab pandas normalize

normalize : bool, {‘all’, ‘index’, ‘columns’}, or {0,1}, default False
Normalize by dividing all values by the sum of values.

If passed ‘all‘ or True, will normalize overall values.
If passed ‘index‘ will normalize over each row.
If passed ‘columns‘ will normalize over each column.
If margins is True, will also normalize margin values.

Crosstab pandas normalize percentage

• In normalize, percentage values are divided by the sum of all values across the DataFrame.
• A key benefit of the crosstab function over the Pandas Pivot Table function is that it allows you to normalize the resulting dataframe, returning values displayed as percentages.
• the normalize argument accepts a number of different options:
  • ‘all’ or True – normalizes the values across the entire dataframe (as a percentage of the total across rows and columns)
  • ‘index’ – normalizes across rows
  • ‘columns’ – normalize down columns
• If the margins argument is set to True, the totals will also be normalized.

Crosstab pandas plot

• plot improves visualization, one can get the idea of the data immediately after seeing the plot
• the plot can be in various forms like pie charts, line graphs, bar graph, etc.
• In this section, we have represented all kinds of plots applicable in crosstab pandas.

Crosstab pandas values

• Values are the array of values to aggregate according to the factors.
• It requires aggfunc be specified.

Read: Pandas DataFrame Iterrows

Crosstab pandas count

• count refers to the total number of times a particular thing happened.
• To count the crosstab values we set the value of margins to True.
• In the below implementation of the count program, you can notice that the total vegetarian & non-vegetarian count is displayed with respect to the flavor profile.
• margins_name can be used to change the default name i.e. All
• Another way of counting items is using the count function. You can see the implementation below. The output for both is the same.

Crosstab pandas aggfunc

• aggfunc is an abbreviation of the aggregate function. It takes any function or method and implements it on the value.
• It is necessary to pass value if it is specified.

Pandas crosstab values cannot be used without an aggfunc

• This statement is true, we cannot crosstab values without using an aggfunc.
• There are few articles state that it is possible by using a pivot table, we tried those methods and found that it is not possible. In their example they are not using crosstab values, they are directly performing the method on the dataset.
• In case you find the solution, please write in the comment down below.

Pandas crosstab sort values

• Pandas Sort Values refer to sorting the value either in an ascending or descending order.
• In pandas, sort_values() is used to sort the values of the provided column.
• But, we cannot implement sorting in crosstab as crosstab by default arrange the index and columns in an ascending order & this order can’t be changed.
• In our implementation on the jupyter notebook, we tried to arrange columns in descending order but it didn’t work with crosstab.
• Here are the parameters of sort_values for your reference.

• axis=0 represents rows and axis = 1 represents columns
• ascending=True if set to False will becomes descending.
• inplace=False, in place saves changes into the current variable if set to True.
• kind, refers to the type of sorting like ‘quicksort‘, ‘mergesort‘, ‘heapsort‘, ‘stable‘
• na_position=’last’ or ‘first, default is last, Puts NaNs at the beginning if first; last puts NaNs at the end.
• ignore_indexbool, default False. If True, the resulting axis will be labeled 0, 1, …, n – 1.
• key, callable, optional, Apply the key function to the values before sorting. This is similar to the key argument in the built-in sorted() function, with the notable difference that this key function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column independently.

implementation on jupyter notebook

Pandas crosstab missing values

• It is common to see missing values referred to as NaN while performing crosstab.
• dropna is used used to drop rows with missing values. Set dropna to True to drop all the missing values.

Pandas crosstab null values

• Null values also referred to as missing values.
• read our section on pandas crosstab missing values

Pandas crosstab count unique values

• df.nunique() Count distinct observations over the requested axis.
• it can be implemented with both DataFrames and Series.
• it returns Series with a number of distinct observations and can ignore NaN values if dropna is set to True.
• Parameters:
  • axis : {0 or ‘index’, 1 or ‘columns’}, default
    The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
  • dropna: bool, default True
    Don’t include NaN in the counts.

Implementation on jupyter notebook:

Pandas crosstab fill values

• While working with crosstab it is important to provide the aggregate function with the values.
• aggregate function holds a command that will be implemented on each item of the value.
• you can perform mean, median, mode, increment or decrement present value, etc operations are possible.

Pandas crosstab default value

• by default crosstab has value set to None.
• It is necessary to pass aggfunc while using Values.

You may like the following Python tutorials:

• Machine Learning using Python
• Pandas in Python
• Python Pandas CSV Tutorial
• Missing Data in Pandas in Python

In this tutorial, we learned about Python pandas crosstab with a few example.

• crosstab pandas python
• crosstab pandas example
• crosstab pandas dataframe
• crosstab pandas sum
• crosstab pandas normalize
• crosstab pandas normalize percentage
• crosstab pandas plot
• crosstab pandas values
• crosstab pandas count
• crosstab pandas aggfunc
• pandas crosstab values cannot be used without an aggfunc
• pandas crosstab sort values
• pandas crosstab missing values
• pandas crosstab null values
• pandas crosstab count unique values
• pandas crosstab fill values
• pandas crosstab default value