Master Pandas: From Installation to Data Analysis in Python

Pandas (derived from the first letters of Panel, DataFrame, Series) is a powerful Python library for data processing, statistical analysis, visualization, and export.

Installation and Import

You can install Pandas via the command line: pip install pandas If you use Anaconda, Pandas is already installed. Import it with the common alias:

import pandas as pd

Data Import

Load external CSV or Excel files using pd.read_csv or pd.read_excel. Example: car = pd.read_csv('data/car-sales.csv') The file path is relative to the script location, and the data is automatically converted into a DataFrame.

Generating a DataFrame

Create a DataFrame from a dictionary of lists:

make = ['Toyota', 'Honda', 'Toyota', 'BMW', 'Nissan', 'Toyota', 'Honda', 'Honda', 'Toyota', 'Nissan']
color = ['White', 'Red', 'Blue', 'Black', 'White', 'Green', 'Blue', 'Blue', 'White', 'White']
odometer = [150043, 87899, 32549, 11179, 213095, 99213, 45698, 54738, 60000, 31600]
doors = [4, 4, 3, 5, 4, 4, 4, 4, 4, 4]
price = ['$4,000.00', '$5,000.00', '$7,000.00', '$22,000.00', '$3,500.00', '$4,500.00', '$7,500.00', '$7,000.00', '$6,250.00', '$9,700.00']
car = pd.DataFrame({'Make': make, 'Colour': color, 'Odometer (KM)': odometer, 'Doors': doors, 'Price': price})

The resulting table is shown in the Jupyter notebook:

Data Description

Use the describe method for quick statistical summaries:

car.describe()

By default it describes numeric columns; to include categorical data, set the include parameter, e.g., car.describe(include=['object', 'float', 'int']). Missing values appear as NaN.

Data Indexing

Extracting Columns

Use bracket notation similar to dictionaries:

car['Price']  # single column
car[['Make', 'Colour']]  # multiple columns

Using loc and iloc

loc

indexes by label, while iloc indexes by integer position. Example: retrieve the value at row index 1 and column "Odometer (KM)": car.loc[1, 'Odometer (KM)'] Or using integer positions: car.iloc[1, 2] Use : to select all rows or columns, e.g., all values in the "Odometer (KM)" column:

car.loc[:, 'Odometer (KM)']
car.iloc[:, 2]

Using apply

Apply a function to transform a column. To clean the Price column by removing the dollar sign and commas and converting to float:

def to_num(x):
    x_new = x.replace('$', '')
    x_new = x_new.replace(',', '')
    return float(x_new)

car['Price'].apply(to_num)

This converts the price strings into numeric values for further analysis.