What is Dataframe?

• Pandas DataFrame is two-dimensional tabular data structure with labeled axes (rows and columns)
• A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns
• DataFrame is similar to table (rows & columns)
• DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables
• DataFrames make manipulating your data easy, from selecting or replacing columns and indices to reshaping your data
• Features of DataFrame (Courtesy: tutorialspoint.com/python_pandas_dataframe)
  • Size – Mutable
  • Labeled axes (rows and columns)
  • Potentially columns are of different types
  • Can Perform Arithmetic operations on rows and columns

Components

• index (rows)
• columns (series)
• data

Overview

Accessing series

Syntax 1: dataframe['SeriesName']
Syntax 2: dataframe.SeriesName

import pandas as pd

# dataframe
df = pd.read_csv("foo.csv")

Single series

df["bar"]

Multiple series

df[ ["baz", "bax"] ]    # returns Dataframe

Sub-dataframe

import pandas as pd

# dataframe
df = pd.read_csv("foo.csv")

# sub-dataframe from dataframe
subdf = df[ ["Bar", "Baz"] ]

type(subdf)    # pandas.core.frame.DataFrame

Shape

• dataframe.shape => a tuple
• Tuple: (numberOfRows, numberOfColumns) (zero-based index to access tuple elements)
  • Row count: df.shape[0]
  • Column count: df.shape[1]

import pandas as pd
df = pd.read_csv("foo.csv")

shape = df.shape

rowCount = shape[0]
colCount = shape[1]

print(f"rowCount: {rowCount}")
print(f"colCount: {colCount}")

Head and tail

• df.head(n) => first n rows
• df.tail(n) => last n rows
• default value of n: 5 (for both head and tail)
  • df.head(n) => first 5 rows
  • df.tail(n) => last 5 rows

Info

• Provides a summary of the data frame including the number of entries, the data type, and the number of non-null entries for each series in the data frame
• Syntax: dataframe.info()