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# Descriptive Statistics and Value Counts

After cleaning and preparing your DataFrame, the next step is to explore the *distribution* and *summary* of your data.

Pandas offers straightforward yet powerful tools for generating quick statistical overviews — helping you identify trends, anomalies, and insights with ease.

<br/>

## Descriptive Methods

Use `.describe()` to get a quick statistical summary of all numeric columns:

- Count of non-null values  
- Mean and standard deviation  
- Minimum and maximum values  
- 25%, 50%, and 75% percentiles  

This method is your go-to tool for **initial data exploration and profiling**.

<br/>

## Categorical Analysis with `value_counts()`

To summarize non-numeric (categorical) columns, use `.value_counts()`.

It returns the frequency of each unique value in a column.

```python title="value_counts() example"
df = pd.DataFrame({
    "Category": ["A", "A", "B", "B", "C", "C"]
})

df["Category"].value_counts()

# Output:
# B    2
# A    2
# C    2
```

<br/>

## Common Additional Methods

| Method        | Purpose                  |
|--------------|--------------------------|
| `mean()`     | Average value            |
| `median()`   | Middle value             |
| `std()`      | Standard deviation       |
| `min()` / `max()` | Minimum and maximum values |
| `sum()`      | Total sum of column      |
| `count()`    | Number of non-null entries |

You can apply these methods to individual columns or across the entire DataFrame to gain a deeper statistical understanding of your dataset.

.describe() is a powerful method in pandas that provides a comprehensive summary of numeric data. It includes various statistics such as mean, max, min, standard deviation, and percentiles, which are crucial for initial data profiling and understanding the distribution of your dataset.

Method	Purpose
`mean()`	Average value
`median()`	Middle value
`std()`	Standard deviation
`min()` / `max()`	Minimum and maximum values
`sum()`	Total sum of column
`count()`	Number of non-null entries

Descriptive Statistics and Value Counts

Descriptive Methods

Categorical Analysis with value_counts()

Common Additional Methods

Which method in pandas is used to get a quick statistical summary of all numeric columns in a DataFrame?

Categorical Analysis with `value_counts()`