Pandas support three kinds of data structures. They are Series, Data Frame, and Panel. A Data frame is a two-dimensional data structure, Here data is stored in a tabular format which is in rows and columns. We can create a data frame in many ways.
Here we are creating a data frame using a list data structure in python.
# import required module import pandas
# assign data l = [ "vignan" , "it" , "sravan" , "subbarao" ]
# create data frame df = pandas.DataFrame(l)
# display dataframe df |
Output:
Here in the above example, we created a data frame. Let’s merge the two data frames with different columns. It is possible to join the different columns is using concat() method.
Syntax: pandas.concat(objs: Union[Iterable[‘DataFrame’], Mapping[Label, ‘DataFrame’]], axis=’0′, join: str = “‘outer'”)
- DataFrame: It is dataframe name.
- Mapping: It refers to map the index and dataframe columns
- axis: 0 refers to the row axis and1 refers the column axis.
- join: Type of join.
Note: If the data frame column is matched. Then empty values are replaced by NaN values.
Steps by step Approach:
- Open jupyter notebook
- Import necessary modules
- Create a data frame
- Perform operations
- Analyze the results.
Below are some examples based on the above approach:
Example 1
In this example, we are going to concatenate the marks of students based on colleges.
# importing pandas module import pandas as pd
# dictionary with list object in # values ie college details details = {
'Name' : [ 'Sravan' , 'Sai' , 'Mohan' , 'Ishitha' ],
'College' : [ 'Vignan' , 'Vignan' , 'Vignan' , 'Vignan' ],
'Physics' : [ 99 , 76 , 71 , 93 ],
'Chemistry' : [ 97 , 67 , 65 , 89 ],
'Data Science' : [ 93 , 65 , 65 , 85 ]
} # converting to dataframe using DataFrame() df = pd.DataFrame(details)
# print data frame df |
Output:
# creating another data details1 = {
'Name' : [ 'Harsha' , 'Saiteja' , 'abhilash' , 'harini' ],
'College' : [ 'vvit' , 'vvit' , 'vvit' , 'vvit' ],
'Physics' : [ 69 , 76 , 51 , 43 ],
'Chemistry' : [ 67 , 67 , 55 , 89 ],
'Maths' : [ 73 , 65 , 61 , 85 ]
} # create dataframe df1 = pd.DataFrame(details1)
# display dataframe df1 |
Output:
# concat dataframes pd.concat([df, df1], axis = 0 , ignore_index = True )
|
# concat when axis = 1 pd.concat([df, df1], axis = 1 , ignore_index = True )
|
Example 2:
Storing marks and subject details
# Import pandas library import pandas as pd
# initialize list of lists data = [[ 'sravan' , 98.00 ], [ 'jyothika' , 90.00 ], [ 'vijay' , 79.34 ]]
# Create the pandas DataFrame df = pd.DataFrame(data, columns = [ 'Name' , 'Marks' ])
# print dataframe. df |
Output:
# initialize list of lists data1 = [[ 'Haseen' , 88.00 , 5 ], [ 'ramya' , 54.00 , 5 ], [ 'haritha' , 56.34 , 4 ]]
# Create the pandas DataFrame df1 = pd.DataFrame(
data1, columns = [ 'Name' , 'Marks' , 'Total subjects registered' ])
# print dataframe. df1 |
Output:
# concatenating data frame pd.concat([df, df1], axis = 0 , ignore_index = True )
|
Output: