1. Transforming Data

Introducing DataFrame

• Pandas is a python package for data manipulation and data visualization
• Pandas is built on NumPy and Matplotlib
  1. NumPy provides multidimension array objects for easy data manipulation that pandas used to store data
  2. Matplotlib has powerful data visualization capabilities
• Rectangular (tabular) data is the most common form to store data
• In pandas, rectangular data is represented as DataFrame object

Exploring a DataFrame: df.head()

• Return the first few rows of the DataFrame

import pandas as pd

print(df.head()) # Print the head of the data

Exploring a DataFrame: df.tail()

• Return the last few rows of the DataFrame

import pandas as pd

print(df.tail()) # Print the tail of the data

Exploring a DataFrame: df.info()

• Displays the names of columns, the data types, and any missing values
• Shows information on each of the columns, such as the data type and number of missing values

import pandas as pd

print(df.info()) # Print information about the data

Exploring a DataFrame: df.shape

• Contains a tuple that holds the number of rows followed by the number of columns
• Returns the number of rows and columns of the DataFrame

import pandas as pd

print(df.shape) # Print the shape of the data

Exploring a DataFrame: df.describe()

• Computes summary statistics for numerical columns, like mean and median
• Calculates a few summary statistics for each column

import pandas as pd

print(data.describe()) # Print a description of the data

Components of a DataFrame: df.values

• DataFrames consist of three different components, accessible using attributes
• The values attribute contains the data values in a 2-dimensional NumPy array

import pandas as pd

print(df.values) # Print the values of the data

Components of a DataFrame: df.columns and df.index

• The other two components of a DataFrame are labels for columns and rows
• The columns attribute contains column names, and the index attribute contains row numbers or row names

import pandas as pd

print(df.columns) # Print the column index of the data

print(df..index) # Print the row index of the data