Master Pandas: Install, Import Data, and Perform Powerful Data Analysis

Pandas library (named from its core data structures Pan el, Da taFrame, S eries) is a powerful data processing and visualization library that supports importing data, statistical analysis, visualization, and export.

Installing and Importing Pandas

You can install Pandas via the command line with: pip install pandas If you use Anaconda, Pandas is already installed. It is commonly imported as pd. import pandas as pd Pandas is often used together with Matplotlib for plotting.

Data Import

External CSV and Excel files can be loaded with pd.read_csv and pd.read_excel. For example: car = pd.read_csv('data/car-sales.csv') The file path is relative to the running script, and the data is loaded into a DataFrame.

Creating a DataFrame

You can create a DataFrame from dictionaries:

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 DataFrame is displayed in Jupyter Notebook.

Data Description

Pandas provides a describe method for quick statistical summaries:

car.describe()

By default it describes numeric columns; to include categorical data use include=['object','float','int']. Missing values appear as NaN.

Data Indexing

Selecting Single or Multiple Columns

Column selection works like dictionary indexing:

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

Using loc and iloc

loc

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

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

Using apply

The apply method applies a function to a column. To clean the Price column by removing dollar signs 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)