> 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/data-types.md).

# Data Types

### Main Data Types

There are four main types of data:

### 1. Categorical Data

Categorical data represents classifications or labels. Pandas has a special data type called `category` to optimize memory usage and performance.

### 2. Numeric Data

Numeric data represents numerical values and is used for computations and analysis.

### 3. Temporal Data

Temporal data represents specific times or durations. These are typically stored as `datetime` objects in Pandas.

### 4. Geographic Data

Geographic data represents location-related information, such as coordinates or region names. While Pandas does not have a specific data type for geographic data, it can be represented as strings or numerical values.

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### Pandas Data Types

In Pandas, objects primarily have the following data types:

1. **Numeric**:
   * **int64**: For integer numbers.
   * **float64**: For floating-point numbers.
   * **complex**: For complex numbers (less common).
2. **String/Object**:
   * **object**: Typically used for string or mixed data types (strings and numbers). It’s the default data type for text data in Pandas.
3. **Boolean**:
   * **bool**: Represents `True` and `False` values. In visualization, Boolean data is viewed as categorical data.
4. **Datetime**:
   * **datetime64\[ns]**: For dates and times, with nanosecond precision. 
5. **Timedelta**:
   * **timedelta64\[ns]**: For differences between datetime values.
6. **Categorical**:
   * **category**: Represents categorical data, which can save memory and improve performance when working with repeated values.

Geographic data is not represented as a different type of data in Pandas DataFrame.

#### Example of Data Types in a DataFrame

```python
import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],       # String/Object
    'Age': [25, 30, 35],                      # int64
    'Height': [5.5, 6.0, 5.8],                # float64
    'IsStudent': [True, False, False],        # bool
    'JoinDate': ['2023-01-01', '2023-02-01', '2023-03-01']  # datetime64
}

df = pd.DataFrame(data)

# Convert 'JoinDate' to datetime
df['JoinDate'] = pd.to_datetime(df['JoinDate'])

# Display data types
print(df.dtypes)
```

#### Output

```csharp
csharpCopy codeName                 object
Age                   int64
Height              float64
IsStudent              bool
JoinDate     datetime64[ns]
dtype: object
```

These data types allow Pandas to perform optimized operations tailored to the type of data you are working with. If needed, you can use `.astype()` to convert columns to a specific type.

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