**π‘ Problem Formulation:** When working with data in Python’s Pandas library, it’s often necessary to understand the type of data you’re dealing with. This can be critical when performing data transformations or analysis. Users might have a series or dataframe column (‘A’) with mixed data types and want to know its underlying data type represented as a dtype object in Pandas, akin to `object`

, `int64`

, `float64`

, or `bool`

. Their goal is to determine this programmatically.

## Method 1: Using the `dtype`

Attribute on a Series

To retrieve the data type of a series, the `dtype`

attribute is the most direct method. It returns the dtype object of the single-dimensional, homogeneously-typed array. For a given pandas series, `series.dtype`

will disclose the dtype of the underlying data effectively.

Here’s an example:

import pandas as pd # Create a series with mixed data types s = pd.Series([1, 'two', 3.0]) # Get the dtype of the series print(s.dtype)

Output:

object

In this code snippet, we create a Pandas series containing integers, strings, and floats, resulting in mixed data types. By calling the `dtype`

attribute, we get the output `object`

, indicating a mix of data types within the series.

## Method 2: Accessing DataType of a DataFrame Column

For a dataframe column, the approach is similar to that for a series. By selecting a column from the dataframe with its label and accessing its `dtype`

attribute, the dtype of that specific column is revealed.

Here’s an example:

import pandas as pd # Create a dataframe with mixed data types df = pd.DataFrame({'A': [1, 'two', 3.0], 'B': ['x', 'y', 'z']}) # Get the dtype of column 'A' print(df['A'].dtype)

Output:

object

This snippet creates a dataframe with two columns, ‘A’ with mixed types and ‘B’ with strings. We then select column ‘A’ and access its `dtype`

attribute to determine its data type. The result is `object`

, confirming that column ‘A’ contains mixed types.

## Method 3: Using the `dtypes`

Attribute on a DataFrame

To investigate the data types of all columns in a dataframe, the `dtypes`

attribute can be employed. This attribute returns a series with index as column names and corresponding dtype as values. It is an effective way to get an overview of the data types of all columns.

Here’s an example:

import pandas as pd # Create a dataframe with different data types df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.0, 2.0, 3.0], 'C': ['one', 'two', 'three']}) # Get the dtypes of all columns print(df.dtypes)

Output:

A int64 B float64 C object dtype: object

Here, we created a dataframe with columns of specific data types. Using `df.dtypes`

, we obtain a series that lists the data type for each column in the dataframe. It shows ‘A’ is of type `int64`

, ‘B’ is `float64`

, and ‘C’ is an `object`

, housing string data.

## Method 4: Using `info()`

Method

The `info()`

method of a DataFrame can be used not just to display the dtype of each column but also provides additional summary information such as memory usage and the number of non-null values. The dtype for each column is presented alongside the column name.

Here’s an example:

import pandas as pd # Create a dataframe with various data types df = pd.DataFrame({'A': [1, 2, 3], 'B': [True, False, True], 'C': [1.2, 3.4, 5.6]}) # Use the info() method to view data types and more df.info()

Output:

<class 'pandas.core.frame.DataFrame'> RangeIndex: 3 entries, 0 to 2 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 A 3 non-null int64 1 B 3 non-null bool 2 C 3 non-null float64 dtypes: bool(1), float64(1), int64(1) memory usage: 203.0 bytes

The `info()`

method is called on our dataframe, which has integer, boolean, and float columns. This method provides a comprehensive overview of each column, including its non-null count and dtype. It informs us that ‘A’ is an `int64`

, ‘B’ is a `bool`

, and ‘C’ is a `float64`

.

## Bonus One-Liner Method 5: Using `astype()`

for Data Type Conversion

The `astype()`

method of pandas is mainly used to convert column types, but when provided with the `type`

function as an argument, it can also reveal the type of data contained. This is a one-liner trick to get the type information in a less conventional way.

Here’s an example:

import pandas as pd # Dataframe with int and float columns df = pd.DataFrame({'A': [1, 2, 3], 'B': [4.0, 5.5, 6.1]}) # Use astype() to return the dtype of column 'A' print(df['A'].astype(type))

Output:

0 <class 'numpy.int64'> 1 <class 'numpy.int64'> 2 <class 'numpy.int64'> Name: A, dtype: object

In this innovative use of `astype()`

, the dtype of the entire series corresponding to column ‘A’ is shown as a series itself, where each entry represents the numpy data type (represented as a Python class) of the elements.

## Summary/Discussion

**Method 1:**Using`dtype`

Attribute on a Series. Best for single column data. May be misleading for mixed-type series.**Method 2:**Accessing DataType of a DataFrame Column. Simple for checking a single dataframe column. Not suitable for checking all columns simultaneously.**Method 3:**Using`dtypes`

. Ideal for a concise overview of all dataframe columns. Does not provide in-depth data statistics.**Method 4:**Using`info()`

Method. Most informative for data type and data integrity analysis. Output is verbose and not easily accessible programmatically.**Bonus Method 5:**Using`astype()`

for Data Type Conversion. Creative but unconventional. Useful for dynamic type retrieval in a looping context.