Data analysis using Pandas
Last Updated :
11 Jul, 2025
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
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:
- A Scalar value which can be integerValue, string
- A Python Dictionary which can be Key, Value pair
- 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 dataConvert 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 NdarrayPandas 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:
- One or more dictionaries
- One or more Series
- 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 dictionariesConvert 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 seriesConvert 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
