Sorting DataFrame values
Sort by column
The sort_values( ) are the values of the column whose name is passed in the by attribute in the ascending order by default we can set this attribute to false to sort the array in the descending order.
Python3
data_frame.sort_values(by='Age', ascending=False).head() |
Output:
CustomerID Genre Age Annual Income (k$) Spending Score (1-100) \
70 71 Male 70 49 55
60 61 Male 70 46 56
57 58 Male 69 44 46
90 91 Female 68 59 55
67 68 Female 68 48 48
NewColumn
70 1
60 1
57 1
90 1
67 1
Sort by multiple columns
Python3
data_frame.sort_values(by=['Age','Annual Income (k$)']).head(10) |
Output:
CustomerID Genre Age Annual Income (k$) Spending Score (1-100) \
33 34 Male 18 33 92
65 66 Male 18 48 59
91 92 Male 18 59 41
114 115 Female 18 65 48
0 1 Male 19 15 39
61 62 Male 19 46 55
68 69 Male 19 48 59
111 112 Female 19 63 54
113 114 Male 19 64 46
115 116 Female 19 65 50
NewColumn
33 1
65 1
91 1
114 1
0 1
61 1
68 1
111 1
113 1
115 1
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