Python | Pandas dataframe.info()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.info() function is used to get a concise summary of the dataframe. It comes really handy when doing exploratory analysis of the data. To get a quick overview of the dataset we use the dataframe.info() function.

Syntax: DataFrame.info(verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None)
Parameters :
verbose : Whether to print the full summary. None follows the display.max_info_columns setting. True or False overrides the display.max_info_columns setting. 
buf : writable buffer, defaults to sys.stdout 
max_cols : Determines whether full summary or short summary is printed. None follows the display.max_info_columns setting. 
memory_usage : Specifies whether total memory usage of the DataFrame elements (including index) should be displayed. None follows the display.memory_usage setting. True or False overrides the display.memory_usage setting. A value of ‘deep’ is equivalent of True, with deep introspection. Memory usage is shown in human-readable units (base-2 representation).
null_counts : Whether to show the non-null counts. If None, then only show if the frame is smaller than max_info_rows and max_info_columns. If True, always show counts. If False, never show counts. 

For link to the CSV file used in the code, click here
Example #1: Use info() function to print full summary of the dataframe. 

Python3

# importing pandas as pd
import pandas as pd

# Creating the dataframe
df = pd.read_csv("nba.csv")

# Print the dataframe
df

Let’s print the full summary of the dataframe.

Python3

# to print the full summary
df.info()

Output : 

As we can see in the output, the summary includes list of all columns with their data types and the number of non-null values in each column. we also have the value of rangeindex provided for the index axis. 

Example #2: Use info() function to print a short summary of the dataframe
Note : In order to print the short summary, we can use the verbose parameter and set it to False.

Python3

# importing pandas as pd
import pandas as pd

# Creating the dataframe
df = pd.read_csv("nba.csv")

# Print the short summary of the
# dataframe by setting verbose = False
df.info(verbose = False)

Output : 

As, we can see in the output, the summary is very crisp and short. It is helpful when we have 1000s of attributes in dataframe. 

Example #3: Use info() function to print a full summary of the dataframe and exclude the null-counts.
Note : In order to print the full summary, with null-counts excluded, we can use null-counts parameter and set it to be false.

Python3

# importing pandas as pd
import pandas as pd

# Creating the dataframe
df = pd.read_csv("nba.csv")

# Print the full summary of the dataframe
# with null count excluded
df.info(verbose = True, null_counts = False)

Output : 

As, we can see in the output, the summary is full but null-counts are excluded.