Data Handling using Pandas - I — NCERT Solutions

A Series is a one-dimensional array in Pandas that stores a sequence of values of any data type and has index labels for each value.

Differences:

• 1-D array (NumPy ndarray): stores elements in a one-dimensional form, but uses only integer positions for indexing.
• List: a general Python collection; it does not provide Pandas-style labeled indexing and built-in data analysis methods.
• Dictionary: stores key:value pairs, while a Series stores values with an index. A Series can be created from a dictionary, but it is not the same thing.

A DataFrame is a two-dimensional labelled data structure like a table. It contains rows and columns, and each column may have a different data type.

Difference from a 2-D array:

• A 2-D array usually stores homogeneous data and is accessed mainly by numeric positions.
• A DataFrame can store different data types in different columns and uses row and column labels for easier access and analysis.

DataFrames are closely related to Series.

• A DataFrame can be thought of as a collection of Series.
• Each column in a DataFrame is a Series.
• When a DataFrame is created from a dictionary of Series, the resulting rows and columns are formed by combining those Series.

The size means the total number of data values.

• For a Series, size = the number of elements in it.
• For a DataFrame, size = the total number of values in all rows and columns, i.e. rows × columns.

import pandas as pd

EngAlph = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))
print(EngAlph)

import pandas as pd

Vowels = pd.Series([0, 0, 0, 0, 0], index=['a', 'e', 'i', 'o', 'u'])
print(Vowels)
print(Vowels.empty)

Vowels.empty will be False because the Series has 5 values, so it is not empty.

import pandas as pd

Friends = pd.Series({'Aman': 12, 'Riya': 15, 'Kunal': 8, 'Meena': 20, 'Tarun': 11})
print(Friends)

The dictionary keys become the index labels of the Series.

import pandas as pd

MTseries = pd.Series()
print(MTseries)
print(MTseries.empty)

MTseries.empty will be True because the Series has no values, so it is an empty Series.

import pandas as pd
import numpy as np

MonthDays = pd.Series(np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]), index=range(1, 13))
print(MonthDays)

Vowels[:] = 10
print(Vowels)

This sets all values in the Series to 10.

Vowels = Vowels / 2
print(Vowels)

Since all values of Vowels are 10 after the previous step, dividing by 2 gives 5 in each position.

Vowels1 = pd.Series([2, 5, 6, 3, 8], index=['a', 'e', 'i', 'o', 'u'])
print(Vowels1)

Vowels3 = Vowels + Vowels1
print(Vowels3)

This adds the two Series after matching their index labels and stores the result in Vowels3.

Vowels - Vowels1
Vowels * Vowels1
Vowels / Vowels1

These are the commands for subtraction, multiplication and division of Vowels by Vowels1.

Vowels1.index = ['A', 'E', 'I', 'O', 'U']
print(Vowels1)

This changes the index labels of Vowels1 to the given uppercase labels.

Use the index, size, and values attributes for each Series.

EngAlph.ndim, EngAlph.size, EngAlph.values
Vowels.ndim, Vowels.size, Vowels.values
Friends.ndim, Friends.size, Friends.values
MTseries.ndim, MTseries.size, MTseries.values
MonthDays.ndim, MonthDays.size, MonthDays.values

MTseries.name = 'SeriesEmpty'
print(MTseries)

This assigns the Series name SeriesEmpty.

MonthDays.index.name = 'monthno'
Friends.index.name = 'Fname'

This names the index of MonthDays as monthno and that of Friends as Fname.

Use positional indexing. Since the 3rd and 2nd values are at positions 2 and 1:

Friends[[2, 1]]

To display alphabets from 'e' to 'p', use label slicing:

EngAlph['e':'p']

In label slicing, the end label is included.

Use head() to get the first 10 values:

EngAlph.head(10)

Use tail() to get the last 10 values:

EngAlph.tail(10)