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
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
