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 Libraryimport pandas as pd# Create series with Data, and Indexa = 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 dataData = [1, 3, 4, 5, 6, 2, 9]# Creating series with default index valuess = pd.Series(Data)# predefined index valuesIndex = ['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 typesd = pd.Series(dictionary) |
Output:
Dictionary type data
Convert an Array to Pandas Series
Program to Create ndarray series.
Python3
# Defining 2darrayData = [[2, 3, 4], [5, 6, 7]]# Creating series of 2darraysnd = 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 Libraryimport pandas as pd# Create DataFrame with Dataa = 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 1dict1 = {'a': 1, 'b': 2, 'c': 3, 'd': 4}# Define Dictionary 2dict2 = {'a': 5, 'b': 6, 'c': 7, 'd': 8, 'e': 9}# Define Data with dict1 and dict2Data = {'first': dict1, 'second': dict2}# Create DataFramedf = 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 pddata_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 1s1 = 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 3s3 = pd.Series(['a', 'b', 'c', 'd', 'e']) # Define DataData ={'first':s1, 'second':s2, 'third':s3}# Create DataFramedfseries = 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 Libraryimport pandas as pd# Define 2d array 1d1 =[[2, 3, 4], [5, 6, 7]]# Define 2d array 2d2 =[[2, 4, 8], [1, 3, 9]]# Define DataData ={'first': d1, 'second': d2}# Create DataFramedf2d = pd.DataFrame(Data) df2d |
Output:
DataFrame with 2d ndarray
