In this tutorial, you’ll learn how to use the Pandas describe method, which is used to computer summary descriptive statistics for your Pandas Dataframe. By the end of reading this tutorial, you’ll have learned how to use the Pandas `.describe()`

method to generate summary statistics and how to modify it using its different parameters, to make sure you get the results you’re hoping for.

Being able to understand your data using high-level summary statistics is an important first step in your exploratory data analysis (EDA). It’s a helpful first step in your data science work, that opens up your work to statistics you may want to explore further.

The Pandas `.describe()`

method provides you with generalized descriptive statistics that summarize the central tendency of your data, the dispersion, and the shape of the dataset’s distribution. It also provides helpful information on missing `NaN`

data.

**The Quick Answer: Pandas describe Provides Helpful Summary Statistics**

## Loading a Sample Pandas Dataframe

If you want to follow along with the tutorial on the Pandas describe method, feel free to copy the code below. The code will generate a dataframe based on the Seaborn library (which I cover off in great detail here). The library provides a number of datasets to guide you through different scenarios. These datasets are accessible via the `load_dataset()`

function.

If you don’t have Seaborn installed, you can install it using either pip or conda. To install it with pip, simply write `pip install seaborn`

into your terminal.

Let’s load a sample dataframe to follow along with:

```
# Loading a sample Pandas dataframe
from seaborn import load_dataset
df = load_dataset('penguins')
print(df.head())
# Returns:
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex
0 Adelie Torgersen 39.1 18.7 181.0 3750.0 Male
1 Adelie Torgersen 39.5 17.4 186.0 3800.0 Female
2 Adelie Torgersen 40.3 18.0 195.0 3250.0 Female
3 Adelie Torgersen NaN NaN NaN NaN NaN
4 Adelie Torgersen 36.7 19.3 193.0 3450.0 Female
```

We can see, that by printing out the first five records of our dataframe using the Pandas `.head()`

method, that our dataframe has seven columns. Some of these columns are numeric, while others contain string values. However, beyond that, we can’t see much else about the data in the dataframe, such as the distribution of the data itself.

This is where the Pandas describe method comes into play! In the next section, you’ll learn how to generate some summary statistics using the Pandas describe method.

## Understanding the Pandas describe Method

The Pandas describe method is a helpful dataframe method that returns descriptive and summary statistics. The method will return items such:

- The number of items
- Measures of dispersion
- Measures of central tendency
- Percentiles of data
- Maximum and minumum values

Let’s break down the various arguments available in the Pandas `.describe()`

method:

Parameter | Default Value | Description |
---|---|---|

`percentiles=` | [.25, .5, .75] | The percentiles to include in the output. The values should fall between the values of 0 and 1. The values should be formatted in a list-like array of numbers. |

`include=` | None | A white list of the data types to include in the result. Accepts: – ‘all’: include all colums – a list-like array of datatypes to include – None: include all numeric columns |

`exclude=` | None | A black list of of data types to omit from the result. Accept: – a list-like array of datatypes to exclude – None: include all numeric columns |

`datetime_is_numeric=` | False | Whether to treat datetime as numeric, which affects the statistics calculated for the column. (New in v1.1.0) |

`.describe()`

methodLet’s see what happens when we apply the method with default parameters:

```
# Running the Pandas dataframe .describe() method with default parameters
from seaborn import load_dataset
df = load_dataset('penguins')
print(df.describe())
# Returns
# bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
# count 342.000000 342.000000 342.000000 342.000000
# mean 43.921930 17.151170 200.915205 4201.754386
# std 5.459584 1.974793 14.061714 801.954536
# min 32.100000 13.100000 172.000000 2700.000000
# 25% 39.225000 15.600000 190.000000 3550.000000
# 50% 44.450000 17.300000 197.000000 4050.000000
# 75% 48.500000 18.700000 213.000000 4750.000000
# max 59.600000 21.500000 231.000000 6300.000000
```

We can that for numeric columns, the dataframe returns the key summary statistics described above.

Similarly, if you only wanted to describe a single column, then you could apply the `.describe()`

method to a Pandas series (or column). Let’s see what this looks like:

```
print(df['body_mass_g'].describe())
# Returns:
# count 342.000000
# mean 4201.754386
# std 801.954536
# min 2700.000000
# 25% 3550.000000
# 50% 4050.000000
# 75% 4750.000000
# max 6300.000000
# Name: body_mass_g, dtype: float64
```

In the next section, you’ll learn how to change the percentiles of the data returned, using the `percentiles=`

parameter.

## Specifying Percentiles in Pandas Describe

The percentile in descriptive statistics is used to identify how many of the values in the series are less than the given percentile. If we, for example, identify a value for the 75^{th} percentile, we indicate that 75% of the values are below that value.

By default, Pandas assigns the percentiles of `[.25, .5, .75]`

meaning that we get values for the 25^{th}, 50^{th}, and 75^{th} percentiles.

We can pass in any array of numbers, as long as the values are all between 0 and 1. Let’s see how we can change this to identify percentiles, namely 10%, 50% and 90%:

```
print(df.describe(percentiles=[.1, .5, .9]))
# Returns
# bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
# count 342.000000 342.000000 342.000000 342.000000
# mean 43.921930 17.151170 200.915205 4201.754386
# std 5.459584 1.974793 14.061714 801.954536
# min 32.100000 13.100000 172.000000 2700.000000
# 10% 36.600000 14.300000 185.000000 3300.000000
# 50% 44.450000 17.300000 197.000000 4050.000000
# 90% 50.800000 19.500000 220.900000 5400.000000
# max 59.600000 21.500000 231.000000 6300.000000
```

We can see by specifying the percentiles, we’re able to modify which descriptive statistics are returned. This allows us to see different spreads of data across our dataframe.

In the next section, you’ll learn how to specify the data types of the columns you want to include.

Check out some other Python tutorials on datagy, including our complete guide to styling Pandas and our comprehensive overview of Pivot Tables in Pandas!

## Specifying Dataframe Columns to Include with Pandas Describe

By default, the Pandas describe method will only include numeric columns. This, in part, is because only numeric values can be used to calculate a mean or percentiles. **The argument allows us to pass in columns such as 'all', which will include all columns**. It also allows us to pass in a list of different data types to include. This can be helpful, for example, when you have coded numerical columns and don’t want to include them.

Let’s see how we can change the methods behaviour to include all columns:

```
print(df.describe(include='all'))
# Returns
# species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex
# count 344 344 342.000000 342.000000 342.000000 342.000000 333
# unique 3 3 NaN NaN NaN NaN 2
# top Adelie Biscoe NaN NaN NaN NaN Male
# freq 152 168 NaN NaN NaN NaN 168
# mean NaN NaN 43.921930 17.151170 200.915205 4201.754386 NaN
# std NaN NaN 5.459584 1.974793 14.061714 801.954536 NaN
# min NaN NaN 32.100000 13.100000 172.000000 2700.000000 NaN
# 25% NaN NaN 39.225000 15.600000 190.000000 3550.000000 NaN
# 50% NaN NaN 44.450000 17.300000 197.000000 4050.000000 NaN
# 75% NaN NaN 48.500000 18.700000 213.000000 4750.000000 NaN
# max NaN NaN 59.600000 21.500000 231.000000 6300.000000 NaN
```

We can see now that all columns are included in the describe method’s output. We can see that this actually this includes different metrics, such as `unique`

and `top`

.

## Treat DateTime Columns as Numeric in Pandas Describe

In Pandas version 1.1, a new argument was introduced. This argument, `datetime_isnumeric=`

, allows us to treat datetime values as numeric, rather than as string values.

Let’s load a different dataframe so that we can see how this argument works. We’ll leave the value set to the default and then toggle it to `True`

and see how it changes.

```
import pandas as pd
df = pd.DataFrame.from_dict({
'Date': ['2021-12-01', '2021-12-02', '2021-12-03', '2021-12-04', '2021-12-05'],
'Values': [100, 120, 140, 160, 180]
})
print(df.describe())
# Returns:
# Values
# count 5.000000
# mean 140.000000
# std 31.622777
# min 100.000000
# 25% 120.000000
# 50% 140.000000
# 75% 160.000000
# max 180.000000
```

By default, the Date column is not included. Let’s not change the `datetime_isnumeric=`

argument to `True`

and see how this changes the output:

```
print(df['Date'].describe(datetime_is_numeric=True))
# Returns:
# count 5
# unique 5
# top 2021-12-01
# freq 1
# Name: Date, dtype: object
```

We can see that when datetime values are treated as numeric we are able to get some key statistics about them, including the count, number of unique items, and the frequency of the top values.

## Conclusion

In this tutorial, you learned how to use the Pandas `.describe()`

method, which is a helpful method to generate summary, descriptive statistics on your dataframe. You learned how to use the describe method to specify particular percentiles and how to include or exclude columns based on datatypes.

To learn more about the Pandas describe method, check out the official documentation here.

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