Pandas DataFrame describe() Method
Example
Multiply the values for each row with the values from the previous row:
import pandas as pd
data = [[10, 18, 11], [13, 15, 8], [9, 20, 3]]
df = pd.DataFrame(data)
print(df.describe())
Try it Yourself »
Definition and Usage
The describe() method returns description of
the data in the DataFrame.
If the DataFrame contains numerical data, the description contains these information for each column:
count - The number of not-empty values.
mean - The average (mean) value.
std - The standard deviation.
min - the minimum value.
25% - The 25%
percentile*.
50% - The 50% percentile*.
75% - The 75% percentile*.
max
- the maximum value.
*Percentile meaning: how many of the values are less than the given percentile. Read more about percentiles in our Machine Learning Percentile chapter.
Syntax
dataframe.describe(percentiles, include, exclude,
datetime_is_numeric)
Parameters
The
percentile, include,
exclude, datetime_is_numeric parameters are
keyword arguments.
| Parameter | Value | Description |
|---|---|---|
| percentile | numbers between: 0 and 1 |
Optional, a list of percentiles to include in the result, default is :[.25, .50, .75]. |
| include | Nonedatatypes |
Optional, a list of the data types to allow in the result |
| exclude | Nonedatatypes |
Optional, a list of the data types to disallow in the result |
| datetime_is_numeric | True |
Optional, default False. Set to True to treat datetime data as numeric |
Return Value
A DataFrame object with statistics for each row.
