Data analysis using Pandas

Pandas are the most popular python library that is used for data analysis. It provides highly optimized performance with back-end source code purely written in C or Python. 

We can analyze data in Pandas with:

• Pandas Series
• Pandas DataFrames

Pandas Series

Series in Pandas is one dimensional(1-D) array defined in pandas that can be used to store any data type.

Creating Pandas Series

Python3

# Program to create series

# Import Panda Library
import pandas as pd

# Create series with Data, and Index
a = pd.Series(Data, index=Index)

Here, Data can be:

1. A Scalar value which can be integerValue, string
2. A Python Dictionary which can be Key, Value pair
3. A Ndarray

Note: Index by default is from 0, 1, 2, …(n-1) where n is the length of data.  

Create Series from List

 Creating series with predefined index values.

Python3

# Numeric data
Data = [1, 3, 4, 5, 6, 2, 9]

# Creating series with default index values
s = pd.Series(Data)

# predefined index values
Index = ['a', 'b', 'c', 'd', 'e', 'f', 'g']

si = pd.Series(Data, Index)

Output:

Create Pandas Series from Dictionary

Program to Create Pandas series from Dictionary.

Python3

dictionary = {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}

# Creating series of Dictionary type
sd = pd.Series(dictionary)

Output:

Dictionary type data

Convert an Array to Pandas Series

Program to Create ndarray series.

Python3

# Defining 2darray
Data = [[2, 3, 4], [5, 6, 7]]

# Creating series of 2darray
snd = pd.Series(Data)

Output:

Data as Ndarray

Pandas DataFrames

The DataFrames in Pandas is a two-dimensional (2-D) data structure defined in pandas which consists of rows and columns.

Creating a Pandas DataFrame

Python3

# Program to Create DataFrame

# Import Library
import pandas as pd

# Create DataFrame with Data
a = pd.DataFrame(Data)

Here, Data can be:

1. One or more dictionaries
2. One or more Series
3. 2D-numpy Ndarray

Create a Pandas DataFrame from multiple Dictionary

Program to Create a Dataframe with two dictionaries.

Python3

# Define Dictionary 1
dict1 = {'a': 1, 'b': 2, 'c': 3, 'd': 4}

# Define Dictionary 2
dict2 = {'a': 5, 'b': 6, 'c': 7, 'd': 8, 'e': 9}

# Define Data with dict1 and dict2
Data = {'first': dict1, 'second': dict2}

# Create DataFrame
df = pd.DataFrame(Data)

df

Output:

DataFrame with two dictionaries

Convert list of dictionaries to a Pandas DataFrame

Here, we are taking three dictionaries and with the help of from_dict() we convert them into Pandas DataFrame.

Python3

import pandas as pd
data_c = [
 {'A': 5, 'B': 0, 'C': 3, 'D': 3},
 {'A': 7, 'B': 9, 'C': 3, 'D': 5},
 {'A': 2, 'B': 4, 'C': 7, 'D': 6}]

pd.DataFrame.from_dict(data_c, orient='columns')

Output:

    A    B    C    D
0    5    0    3    3
1    7    9    3    5
2    2    4    7    6

Create DataFrame from Multiple Series

Program to create a dataframe of three Series.

Python3

import pandas as pd

# Define series 1
s1 = pd.Series([1, 3, 4, 5, 6, 2, 9])

# Define series 2
s2 = pd.Series([1.1, 3.5, 4.7, 5.8, 2.9, 9.3])

# Define series 3
s3 = pd.Series(['a', 'b', 'c', 'd', 'e'])

# Define Data
Data ={'first':s1, 'second':s2, 'third':s3}

# Create DataFrame
dfseries = pd.DataFrame(Data)

dfseries

Output:

DataFrame with three series

Convert a Array to Pandas Dataframe

One constraint has to be maintained while creating a DataFrame of 2D arrays – The dimensions of the 2D array must be the same.

Python3

# Program to create DataFrame from 2D array

# Import Library
import pandas as pd

# Define 2d array 1
d1 =[[2, 3, 4], [5, 6, 7]]

# Define 2d array 2
d2 =[[2, 4, 8], [1, 3, 9]]

# Define Data
Data ={'first': d1, 'second': d2}

# Create DataFrame
df2d = pd.DataFrame(Data)

df2d

Output:

DataFrame with 2d ndarray