> For the complete documentation index, see [llms.txt](https://larhues-personal-organization.gitbook.io/intro-to-data-visualization/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://larhues-personal-organization.gitbook.io/intro-to-data-visualization/getting-started/reviewing-data.md).

# Reviewing Data

When working with a DataFrame, it's important to understand its structure and contents. Pandas provides several functions to help review a DataFrame.

#### Viewing the Top and Bottom Rows

One way to explore a new dataset is to preview the DataFrame. To preview a DataFrame by seeing the first few rows or the last few rows, use the head() and tail() functions: 

```
# View the first 5 rows
data.head()

# View the last 5 rows
data.tail()
```

You can specify the number of rows to view, e.g., `data.head(10)` to see the first 10 rows or `data.tail(10)` to see the last 10 rows.

#### Getting Basic Information

Use `info()` to get an overview of the DataFrame:

```
# Overview of data types, non-null values, and memory usage
data.info()
```

#### Descriptive Statistics

The `describe()` method provides summary statistics for *numerical* columns:

```
# Summary statistics
data.describe()
```

This includes metrics like mean, standard deviation, min, max, and percentiles.

#### Value Counts

Use `value_counts()` to see the distribution of values in a specific column of categorical values:

```
# Count unique values in a column
data['Category'].value_counts()
```

#### Checking for Missing Values

Most datasets contain missing values. To identify missing data:

```
# Check for missing values
data.isnull().sum()
```

#### Shape and Columns

To get the dimensions and column names of the DataFrame:

```
# Number of rows and columns
data.shape

# Column names
data.columns
```

#### Sample Rows

Sometimes, it is useful to take a random sample of the rows in the dataset. Use the sample() function to randomly sample rows from the DataFrame:

```
# Randomly select 5 rows
data.sample(5)
```

***

### Additional Resources

For additional information, see the Pandas [Cheatsheet](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf).