Printing rows of the Data
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.
Python3
#displaying first five rowsdisplay(data_frame.head())#displaying last five rowsdisplay(data_frame.tail()) |
Output:
CustomerID Genre Age Annual Income (k$) Spending Score (1-100)
0 1 Male 19 15 39
1 2 Male 21 15 81
2 3 Female 20 16 6
3 4 Female 23 16 77
4 5 Female 31 17 40
CustomerID Genre Age Annual Income (k$) Spending Score (1-100)
195 196 Female 35 120 79
196 197 Female 45 126 28
197 198 Male 32 126 74
198 199 Male 32 137 18
199 200 Male 30 137 83
[4]
0s
# Program to print all the column name of the dataframe
print(list(data_frame.columns))
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