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Practice

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.

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.
print(sales) Output Result
0    1000
1    2000
2    1500
3    3000
Name: Sales, dtype: int64

  1. 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.
print(filtered_data) Output Result
        Item  Sales
1     Banana   2000
3     Grapes   3000

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


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}')

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