A Python Library Specialized in Data Processing, Pandas

When working with data such as item sales or customer influx over time, it's typically organized in a table format with rows (horizontal) and columns (vertical).

Pandas is one of the most widely used packages in Python for handling tabular data.

With Pandas, you can systematically perform a variety of tasks—from basic operations like loading and saving data to more complex ones like filtering, sorting, and statistical analysis.

You can install Pandas using the following command. However, in the practice environment, Pandas is already installed, so no additional installation is needed.

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pip install pandas

The core data structures of Pandas are Series and DataFrame.

A Series is a one-dimensional data structure, conceptually similar to a single column in Excel.

Data is arranged sequentially, similar to a Python list (array).

Each piece of data has a unique index (identifier indicating the position of the data), through which the data can be accessed.

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import pandas as pd

# Create a series
data_series = pd.Series([10, 20, 30, 40])

print(data_series)
# Output
# 0    10
# 1    20
# 2    30
# 3    40
# dtype: int64

A DataFrame is a two-dimensional data structure composed of multiple Series.

It contains both rows and columns, and each column can hold a different data type.

This structure is similar to an Excel table (spreadsheet).

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import pandas as pd

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

print(data_frame)
# Output
#        Item  Sales
# 0     Apple   1000
# 1    Banana   2000
# 2 Strawberry  1500
# 3    Grapes   3000

In the example above, the DataFrame is created with columns labeled 'Item' and 'Sales'.

For instance, 'Item': ['Apple', 'Banana', 'Strawberry', 'Grapes'] creates a Series resembling an Excel column, which is then combined with other Series to form the DataFrame.