# Grouping DataFrame by column and calculating mean in Python

## Python Pandas Numpy: Exercise-13 with Solution

Group a Pandas DataFrame by a column and calculate the mean of another column.

**Sample Solution:**

**Python Code:**

```
import pandas as pd
# Create a sample DataFrame
data = {'Category': ['A', 'B', 'A', 'B', 'A', 'B'],
'Values': [100, 200, 300, 400, 500, 600]}
df = pd.DataFrame(data)
# Group by 'Category' and calculate the mean of 'Values'
mean_values = df.groupby('Category')['Values'].mean()
# Display the mean values
print(mean_values)
```

Output:

Category A 300.0 B 400.0 Name: Values, dtype: float64

**Explanation:**

In the exerciser above -

- First we create a sample DataFrame (df) with columns 'Category' and 'Values'.
- The groupby('Category') method groups the DataFrame by the 'Category' column.
- The ['Values'].mean() part calculates the mean of the 'Values' column for each group.
- The result is a Pandas Series with the mean values for each category.

**Flowchart:**

**Python Code Editor:**

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