The maximum number of columns to output. If the number of columns in the source DataFrame exceeds this value, then some columns will be truncated. The default value depends on your machine.

4. memory_usage | string or boolean | optional

Whether or not to show the memory usage of each column:

Value	Description
`True`	Show memory usage. For DataFrames that contain object types (e.g. strings), the memory usage would be not be accurate. This is because the method takes a crude estimate on memory consumed by object types.
`False`	Do not show memory usage.
`"deep"`	Perform some heavy lifting to calculate actual memory usage of object types, and show the memory usage.

The default value depends on your machine.

5. null_counts | boolean | optional

Whether or not to show the number of non-null values in each column. Again, the default value depends on your machine.

Return Value

Nothing is returned since all we're doing here is printing a summary of the DataFrame.

Examples

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[3,4],"B":[5.0,6.0],"C":[True,False],"D":["K","KK"]})
df
                
            
               A  B    C      D
0  3  5.0  True   K
1  4  6.0  False  KK

Basic usage

Calling info() without any parameters:


        
        
            
                
                
                    df.info()
                
            
            <class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 4 columns):
A    2 non-null int64
B    2 non-null float64
C    2 non-null bool
D    2 non-null object
dtypes: bool(1), float64(1), int64(1), object(1)
memory usage: 178.0+ bytes

Here, my machine has the following default options:

verbose=True
memory_usage=True
null_counts=True

Setting verbose=False

If you do not need information about each column, set verbose=False like so:


        
        
            
                
                
                    df.info(verbose=False)
                
            
            <class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Columns: 4 entries, A to D
dtypes: bool(1), float64(1), int64(1), object(1)
memory usage: 178.0+ bytes

Writing to an external file

Instead of showing the output on the screen, we can write the output to an external file by using the buf parameter.


        
        
            
                
                
                    import io

buffer = io.StringIO()
df.info(buf=buffer)
str_summary = buffer.getvalue()

with open("df_summary.txt", "w") as file:  
   file.write(str_summary)

This will create a file called "df_summary.txt" in the same directory as your Python script. The content of this file would just be the same as what you would have seen on the screen.

Setting memory_usage=deep

Since our DataFrame contains a column of data-type object (column D), the value returned by memory_usage=True will be off:


        
        
            
                
                
                    df.info()
                
            
            <class 'pandas.core.frame.DataFrame'>
...
memory usage: 178.0+ bytes

To get a more accurate representation of the memory consumed by the DataFrame, set memory_usage="deep":


        
        
            
                
                
                    df.info(memory_usage="deep")
                
            
            <class 'pandas.core.frame.DataFrame'>
...
memory usage: 287.0 bytes

We see that the our DataFrame occupies 287 bytes so we were off by nearly 100 bytes.

Published by Isshin Inada

Edited by 0 others

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Official Pandas Documentation

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html

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