Lecture

Manipulating Data with DataFrames

A DataFrame in Pandas is a table-like data structure for systematically handling data, similar to Excel.

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 items and sales data and demonstrates data manipulation.

Data Manipulation Example
import pandas as pd # Create DataFrame 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) # Filter rows based on a condition filtered_data = data_frame[data_frame['Sales'] > 1500] print("filtered_data:", filtered_data) # Sort 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, returning it as a series.
Output of print(sales)
0 1000 1 2000 2 1500 3 3000 Name: Sales, dtype: int64

  1. filtered_data = data_frame[data_frame['Sales'] > 1500] filters and creates a new DataFrame with rows where the 'Sales' value is greater than 1500.
Output of print(filtered_data)
Item Sales 1 Banana 2000 3 Grape 3000

  1. sorted_data = data_frame.sort_values(by='Sales', ascending=False) sorts the DataFrame in descending order based on the 'Sales' column.
Output of print(sorted_data)
Item Sales 3 Grape 3000 1 Banana 2000 2 Strawberry 1500 0 Apple 1000

Calculating Maximum, Minimum, and Average Values

Methods for calculating the maximum, minimum, and average values of a specific DataFrame column are as follows:

  • max(): Maximum value
  • min(): Minimum value
  • mean(): Average value

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


Calculating Maximum, Minimum, and Average Values
import pandas as pd data_frame = pd.DataFrame({ 'Item': ['Apple', 'Banana', 'Strawberry', 'Grape'], '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}')

With Pandas, you can easily perform a variety of data manipulations and calculations.


Reference

Mission
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What method is used in pandas to sort a specific column based on a condition?

sort_values()

filter()

groupby()

drop()

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