Summary of Data Frame
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.
Python3
data_frame.info() |
Output:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CustomerID 200 non-null int64
1 Genre 200 non-null object
2 Age 200 non-null int64
3 Annual Income (k$) 200 non-null int64
4 Spending Score (1-100) 200 non-null int64
dtypes: int64(4), object(1)
memory usage: 7.9+ KB
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
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