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Visualizing DataFrame

By using the lambda operator

Conclusion

Scatter plot

The plot( ) function is used to make plots of the data frames.

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# Visualization
data_frame.plot(x ='CustomerID', y='Spending Score (1-100)',kind = 'scatter')
 






                        









													
													
												

												
												

													
													
Output:

													
													
												

												
												

													
													
Scatter plot of the Customer Satisfaction column

													
													
												

												
												

													
													
Histogram

													
													
												

												
												

													
													
The plot.hist( ) function is used to make plots of the data frames.

													
													
												

												
												

													
													




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data_frame.plot.hist()



















                        







                        









													
													
												

												
												

													
													
Output:

													
													
												

												
												

													
													
Histogram for the distribution of the data

													
													
												

												
											

											
											

												
Data Processing with Pandas

												
												

													
													
Data Processing is an important part of any task that includes data-driven work. It helps us to provide meaningful insights from the data. As we know Python is a widely used programming language, and there are various libraries and tools available for data processing. 

													
													
												

												
												

													
													
In this article, we are going to see Data Processing in Python, Loading, Printing rows and Columns, Data frame summary, Missing data values Sorting and Merging Data Frames, Applying Functions, and Visualizing Dataframes. 

													
													
												

												
												

													
													


Table of Content


    

  • What is Data Processing in Python?
    • 

  • What is Pandas?
    • 

  • Loading Data in Pandas DataFrame
    • 

  • Printing rows of the Data
    • 

  • Printing the column names of the DataFrame 
    • 

  • Summary of Data Frame
    • 

  • Descriptive Statistical Measures of a DataFrame
    • 

  • Missing Data Handing
    • 

  • Sorting DataFrame values
    • 

  • Merge Data Frames
    • 

  • Apply Function
    • 

  • By using the lambda operator
    • 

  • Visualizing DataFrame
    • 

  • Conclusion
    • 




													
													
												

												
											

										


										
										

											
Similar Reads

											
											

												
													

														What is Data Processing in Python?

													

														Data processing in Python refers to manipulating, transforming, and analyzing data by using Python. It contains a series of operations that aim to change raw data into structured data. or meaningful insights. By converting raw data into meaningful insights it makes it suitable for analysis, visualization, or other applications.Python provides several libraries and tools that facilitate efficient data processing, making it a popular choice for working with diverse datasets....
													

												
											

											
											

												
													

														What is Pandas?

													

														Pandas is a powerful, fast, and open-source library built on NumPy. It is used for data manipulation and real-world data analysis in Python. Easy handling of missing data, Flexible reshaping and pivoting of data sets, and size mutability make pandas a great tool for performing data manipulation and handling the data efficiently....
													

												
											

											
											

												
													

														Loading Data in Pandas DataFrame

													

														Reading CSV file using pd.read_csv and loading data into a data frame. Import pandas as using pd for the shorthand. You can download the data from here....
													

												
											

											
											

												
													

														Printing rows of the Data

													

														...
													

												
											

											
											

												
													

														Printing the column names of the DataFrame

													

														By default, data_frame.head() displays the first five rows and data_frame.tail() displays last five rows. If we want to get first ‘n’ number of rows then we use, data_frame.head(n) similar is the syntax to print the last n rows of the data frame....
													

												
											

											
											

												
													

														Summary of Data Frame

													

														...
													

												
											

											
											

												
													

														Descriptive Statistical Measures of a DataFrame

													

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# Program to print all the column name of the dataframe
print(list(data_frame.columns))...
													

												
											

											
											

												
													

														Missing Data Handing

													

														...
													

												
											

											
											

												
													

														Sorting DataFrame values

													

														The functions info() prints the summary of a DataFrame that includes the data type of each column, RangeIndex (number of rows), columns, non-null values, and memory usage....
													

												
											

											
											

												
													

														Merge Data Frames

													

														...
													

												
											

											
											

												
													

														Apply Function

													

														The describe() function outputs descriptive statistics which include those that summarize the central tendency, dispersion, and shape of a dataset’s distribution, excluding NaN values. For numeric data, the result’s index will include count, mean, std, min, and max as well as lower, 50, and upper percentiles. For object data (e.g. strings), the result’s index will include count, unique, top, and freq....
													

												
											

											
											

												
													

														By using the lambda operator

													

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														Visualizing DataFrame

													

														Find missing values in the dataset...
													

												
											

											
											

												
													

														Conclusion

													

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												By using the lambda operator
											
											
										

										

											
											
												Conclusion