﻿ Cross-Tabulation in Pandas: Analyzing DataFrame categories

# Cross-Tabulation in Pandas: Analyzing DataFrame categories

## Python Pandas Numpy: Exercise-37 with Solution

Perform a cross-tabulation between two columns in a DataFrame.

Sample Solution:

Python Code:

``````import pandas as pd

# Create a sample DataFrame
data = {'Category': ['A', 'B', 'A', 'B', 'C', 'A', 'C', 'C', 'B', 'A'],
'Value': [10, 15, 20, 25, 30, 35, 40, 45, 50, 55]}

df = pd.DataFrame(data)

# Perform a cross-tabulation between 'Category' and 'Value'
cross_tab = pd.crosstab(df['Category'], df['Value'])

# Display the cross-tabulation
print(cross_tab)
```
```

Output:

```Value     10  15  20  25  30  35  40  45  50  55
Category
A          1   0   1   0   0   1   0   0   0   1
B          0   1   0   1   0   0   0   0   1   0
C          0   0   0   0   1   0   1   1   0   0
```

Explanation:

Here's a breakdown of the above code:

• We create a sample DataFrame (df) with two columns: 'Category' and 'Value'.
• The pd.crosstab(df['Category'], df['Value']) line performs a cross-tabulation between these two columns.
• The resulting "cross_tab" DataFrame shows the frequency of each combination of 'Category' and 'Value'.

Flowchart:

Python Code Editor:

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