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import pandas as pd

data_frame = pd.DataFrame({
    'Item': ['Apple', 'Banana', 'Strawberry', 'Grape'],
    'Sales': [1000, 2000, 1500, 3000]
})

# Select a specific column
sales = data_frame['Sales']
print("sales:", sales)
print("=" * 40)

# Filter rows based on conditions
filtered_data = data_frame[data_frame['Sales'] > 1500]
print("filtered_data:", filtered_data)
print("=" * 40)

# Sort the data
sorted_data = data_frame.sort_values(by='Sales', ascending=False)
print("sorted_data:", sorted_data)
print("=" * 40)

# Manipulating Data with DataFrames

A `DataFrame` in Pandas is a data structure for systematically handling **tabular data**, similar to an Excel spreadsheet.

A DataFrame is a `2-dimensional array` composed of multiple series, with both rows and columns.

Below is a simple code example that creates a DataFrame containing item and sales data and manipulates the data.

```python title="Data Manipulation Example"
import pandas as pd

# Create DataFrame
data_frame = pd.DataFrame({
 'Item': ['Apple', 'Banana', 'Strawberry', 'Grapes'],
 'Sales': [1000, 2000, 1500, 3000]
})

# Select a specific column
sales = data_frame['Sales']
print("sales:", sales)

# Filter rows based on a condition
filtered_data = data_frame[data_frame['Sales'] > 1500]
print("filtered_data:", filtered_data)

# Sort the data
sorted_data = data_frame.sort_values(by='Sales', ascending=False)
print("sorted_data:", sorted_data)
```

 

1. `sales = data_frame['Sales']` selects only the 'Sales' column from the DataFrame and returns it as a series.

```plaintext title="print(sales) Output Result"
0 1000
1 2000
2 1500
3 3000
Name: Sales, dtype: int64
```

 

2. `filtered_data = data_frame[data_frame['Sales'] > 1500]` filters the rows where the value in the 'Sales' column is greater than 1500 and creates a new DataFrame.

```plaintext title="print(filtered_data) Output Result"
 Item Sales
1 Banana 2000
3 Grapes 3000
```

 

3. `sorted_data = data_frame.sort_values(by='Sales', ascending=False)` sorts the DataFrame in descending order based on the 'Sales' column.

```plaintext title="print(sorted_data) Output Result"
 Item Sales
3 Grapes 3000
1 Banana 2000
2 Strawberry 1500
0 Apple 1000
```

 

## Calculating Maximum, Minimum, and Average Values

There are methods to calculate the maximum, minimum, and average values of a specific column in a DataFrame.

- `max()`: Maximum value

- `min()`: Minimum value

- `mean()`: Average value

Here is an example code that calculates the maximum, minimum, and average values of the 'Sales' column.

 

```python title="Calculating Maximum, Minimum, Average Values"
import pandas as pd

data_frame = pd.DataFrame({
 'Item': ['Apple', 'Banana', 'Strawberry', 'Grapes'],
 'Sales': [1000, 2000, 1500, 3000]
})

# Maximum value
max_sales = data_frame['Sales'].max()
# Output: Maximum value: 3000
print(f'Maximum value: {max_sales}')

# Minimum value
min_sales = data_frame['Sales'].min()
# Output: Minimum value: 1000
print(f'Minimum value: {min_sales}')

# Average value
mean_sales = data_frame['Sales'].mean()
# Output: Average value: 1875.0
print(f'Average value: {mean_sales}')
```

When you select a specific column in Pandas, it returns a Series, not a DataFrame.

### Selecting a specific column of a DataFrame using Pandas returns a Series.

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