Importing Pandas Profiling
Python3
# importing packages import pandas as pd from pandas_profiling import ProfileReport |
Now, create the data frame or import any dataset.
Python3
# creating the dataframe # dictionary of data dct = { 'ID' : { 0 : 23 , 1 : 43 , 2 : 12 , 3 : 13 , 4 : 67 , 5 : 89 , 6 : 90 , 7 : 56 , 8 : 34 }, 'Name' : { 0 : 'Ram' , 1 : 'Deep' , 2 : 'Yash' , 3 : 'Aman' , 4 : 'Arjun' , 5 : 'Aditya' , 6 : 'Divya' , 7 : 'Chalsea' , 8 : 'Akash' }, 'Marks' : { 0 : 89 , 1 : 97 , 2 : 45 , 3 : 78 , 4 : 56 , 5 : 76 , 6 : 100 , 7 : 87 , 8 : 81 }, 'Grade' : { 0 : 'B' , 1 : 'A' , 2 : 'F' , 3 : 'C' , 4 : 'E' , 5 : 'C' , 6 : 'A' , 7 : 'B' , 8 : 'B' } } # forming dataframe and printing data = pd.DataFrame(dct) print (data) |
Output:
ID Name Marks Grade
0 23 Ram 89 B
1 43 Deep 97 A
2 12 Yash 45 F
3 13 Aman 78 C
4 67 Arjun 56 E
5 89 Aditya 76 C
6 90 Divya 100 A
7 56 Chalsea 87 B
8 34 Akash 81 B
Pandas Profiling in Python
Pandas is a very vast library that offers many functions with the help of which we can understand our data. Pandas profiling provides a solution to this by generating comprehensive reports for datasets that have numerous features. These reports can be customized according to specific requirements. In this article, we will dive into this library’s functionalities and explore its various features like:
- Installation of Pandas Profiling
- Importing Pandas Profiling
- Generating Profile Report
- Exploring Profile Report Generated
- Overview
- Variables
- Correlations
- Missing Values
- Sample
- Saving the Profile Report
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