Descriptive statistics are used to summarize, analyze, and visualize data with the goal of gaining insight into the underlying patterns. Pandas is a highly popular Python library widely used for data analysis due to its ability to work with tabulated data.

In this article, we will learn how to get descriptive statistics for a Pandas DataFrame.

## Collecting the Data

Before diving into getting descriptive statistics, it is essential to have data. There are various sources of data, such as scraping data from the web, collecting data through surveys, or downloading datasets from public repositories like Kaggle.

When working with Pandas DataFrame, the data can be in CSV, Excel, JSON, or other structured data formats.

## Creating the DataFrame

The next step is to create a DataFrame from the data collected. A DataFrame is an essential data structure in Pandas, and it is essentially a table that contains rows and columns.

To import Pandas and create a DataFrame, use the following syntax:

```
import pandas as pd
df = pd.read_csv('file_name.csv')
```

In this syntax, “pd” is an alias for pandas, and “read_csv” is used to read the CSV file and create a DataFrame from it.

## Getting the Descriptive Statistics for a Specific Column

Descriptive statistics for a specific column can be retrieved from the DataFrame using “.describe()” method. For instance, to get the descriptive statistics for the “Age” column in our DataFrame, we use the following syntax:

`df['Age'].describe()`

The result will display the count, mean, standard deviation, minimum value, 25th percentile, median (50th percentile), 75th percentile, and maximum value for the “Age” column.

## Getting the Descriptive Statistics for the Entire DataFrame

To get descriptive statistics for the entire DataFrame, use “.describe()” method, with “include=’all'”. With “include=’all'” parameter, it ensures that all the columns, including the categorical columns, are included in the output.

### The following syntax is used:

`df.describe(include='all')`

The output will display the descriptive statistics for all columns in the DataFrame. It includes the count, unique, top, and frequency for categorical columns; mean, standard deviation, minimum value, 25th percentile, median (50th percentile), 75th percentile, and maximum values for numerical columns.

## Steps to Get the Descriptive Statistics for Pandas DataFrame

The following are the steps involved in getting descriptive statistics for a Pandas DataFrame.

### Step 1 – Collecting the Data

To get descriptive statistics for a Pandas DataFrame, first, you need data.

Data can be collected from various sources, such as scraping data from the web, collecting data through surveys, or downloading datasets from public repositories like Kaggle.

### Step 2 – Creating the DataFrame

After collecting the data, the next step is to create a DataFrame from the data.

### The following syntax is used to create a DataFrame in Pandas:

```
import pandas as pd
df = pd.read_csv('file_name.csv')
```

In this example, “pd” is an alias for pandas, and “read_csv” is used to read the CSV file and create a DataFrame from it.

### Step 3 – Getting the Descriptive Statistics for a Specific Column

Use the “.describe()” method to get descriptive statistics for a specific column in a Pandas DataFrame.

To get the descriptive statistics for the “Age” column, we use the following syntax:

`df['Age'].describe()`

The output will show the count, mean, standard deviation, minimum value, 25th percentile, median, 75th percentile, and maximum value for the “Age” column.

### Step 4 – Getting the Descriptive Statistics for the Entire DataFrame

To get descriptive statistics for the entire DataFrame, use “.describe()” method, along with “include=’all'”. This ensures all columns, including categorical columns, are included in the output.

### The following syntax is used:

`df.describe(include='all')`

The output will display the descriptive statistics for all columns in the DataFrame, including count, unique, top, frequency for categorical columns and mean, standard deviation, minimum value, 25th percentile, median, 75th percentile, and maximum value for numerical columns.

## Conclusion

In summary, this article has provided an overview of how to get descriptive statistics for a Pandas DataFrame through a four-step approach. Each step, including data collection, DataFrame creation, getting descriptive statistics for a specific column, and getting statistics for the entire DataFrame, has been explained in detail.

With this knowledge, you can analyze data effectively and gain insight into underlying patterns in the data.

Statistical analysis can reveal underlying patterns in data, and it can be used to compare data that have similar characteristics. In this article, we will discuss how to get descriptive statistics for numerical and categorical data in a Pandas DataFrame.

## Descriptive Statistics for Numerical Data

Numerical data can be analysed through descriptive statistics, and we can use the Pandas library for that purpose. Descriptive statistics for numerical data include the median, mean, minimum, and maximum values.

## Getting the Descriptive Statistics for a Numerical Column

To illustrate how to get descriptive statistics for numerical data, we will use a dataset containing the prices of different products. We first need to create a Pandas DataFrame, which we can do by importing a CSV file using the following syntax:

```
import pandas as pd
df = pd.read_csv('file_name.csv')
```

After importing the dataset, we can get the descriptive statistics for the ‘price’ column using the following syntax:

`df['price'].describe()`

The resulting output will show the count, mean, standard deviation, minimum, 25th percentile, median, 75th percentile, and maximum values for the ‘price’ column.

## Converting Float Values to Integer Values

If the dataset contains float values in the numerical column, we can convert them to integer values. Converting float values to integer values can simplify further data analysis, as integer data types consume less memory and are easier to manipulate.

To convert float data type to integer data type, we can use the “astype” method. The following syntax converts all float values in the ‘price’ column to integer values:

`df['price'] = df['price'].astype(int)`

In this example, the .astype method is used to convert float to integer data type.

## Descriptive Statistics for Categorical Data

Categorical data is data that can be grouped into categories. Examples include gender, product type, or state of residence.

In this type of data, there is no intrinsic ordering of categories as there is for numerical data. However, we can still use descriptive statistics to analyze categorical data using the Pandas library.

## Getting the Descriptive Statistics for a Categorical Column

We can use the “.describe()” method to get the descriptive statistics of the categorical column. Continuing with the product dataset, we can get the descriptive statistics for the ‘product’ column using the following syntax:

`df['product'].describe()`

The resulting output will show the count, number of unique values, the most frequently occurring value (‘top’), and the frequency of the most frequently occurring value (‘freq’).

In addition to getting descriptive statistics with “.describe()”, we can also use the “.value_counts()” method to display the frequency distribution of the values in a categorical column. The following syntax can be used to show how many times each product appears in the ‘product’ column:

`df['product'].value_counts()`

This method produces an output showing the frequency of each unique product in the ‘product’ column.

## Conclusion

This article has covered the methods of getting descriptive statistics for numerical and categorical data in a Pandas DataFrame. For numeric data, we showed how to obtain descriptive statistics such as median, mean, minimum, and maximum values.

Additionally, we explained how to convert float data type to integer data type for more straightforward manipulation of data. For categorical data, we illustrated how to use the describe method to compute the statistical characteristics of categorical data and value_counts to display the frequency distribution of categorical data.

This knowledge can greatly aid data analysis and provide invaluable insights for data-driven decision-making.

Descriptive statistics are an essential part of data analysis.

In Pandas DataFrame, descriptive statistics can be used to summarize and analyze data efficiently. In this article, we will cover how to get descriptive statistics for the entire Pandas DataFrame and break down each statistical measure.

## Getting the Descriptive Statistics for the Entire DataFrame

To get the descriptive statistics for the entire Pandas DataFrame, we can use the “.describe()” method. This method computes the following statistical measures for each numerical column in the DataFrame: count, mean, standard deviation, minimum value, 25th percentile, median, 75th percentile, and maximum value.

To illustrate how to get descriptive statistics for the entire Pandas DataFrame, we will use a dataset containing information about the customers of an e-commerce business. We first need to create a Pandas DataFrame, which we can do by importing a CSV file using the following syntax:

```
import pandas as pd
df = pd.read_csv('file_name.csv')
```

After importing the dataset, we can get the descriptive statistics for the entire DataFrame using the following syntax:

`df.describe(include='all')`

The resulting output will show the descriptive statistics for all columns, including categorical columns and numerical columns. For numerical data, the output includes count, mean, standard deviation, minimum value, 25th percentile, median, 75th percentile, and maximum value.

For categorical data, the output includes count, number of unique values, top value, and frequency of top value.

## Breaking Down the Descriptive Statistics

Now that we have seen how to get the descriptive statistics for the entire DataFrame, let’s dive a bit deeper and explore each statistical measure in detail.

## Getting the Count

The “count” statistical measure computes the total number of non-null values in a column. We can get the count for a column using the following syntax:

`df['column_name'].count()`

## Getting the Mean

The “mean” statistical measure computes the arithmetic average of all values in a column. We can get the mean for a column using the following syntax:

`df['column_name'].mean()`

## Getting the Standard Deviation

The “standard deviation” statistical measure computes the amount of variation or dispersion of a set of values from the mean. We can get the standard deviation for a column using the following syntax:

`df['column_name'].std()`

## Getting the Minimum Value

The “minimum value” statistical measure computes the smallest value in a column. We can get the minimum value for a column using the following syntax:

`df['column_name'].min()`

Getting the 0.25 Quantile

The “0.25 quantile” statistical measure represents the value below which approximately 25% of the data falls.

We can get the 0.25 quantile for a column using the following syntax:

`df['column_name'].quantile(q=0.25)`

Getting the Median (0.50 Quantile)

The “median” statistical measure is also known as the 0.50 quantile, and it represents the value below which half of the data falls. We can get the median for a column using the following syntax:

`df['column_name'].quantile(q=0.50)`

Getting the 0.75 Quantile

The “0.75 quantile” statistical measure represents the value below which approximately 75% of the data falls.

We can get the 0.75 quantile for a column using the following syntax:

`df['column_name'].quantile(q=0.75)`

## Getting the Maximum Value

The “maximum value” statistical measure computes the largest value in a column. We can get the maximum value for a column using the following syntax:

`df['column_name'].max()`

## Conclusion

In this article, we have covered how to get descriptive statistics for the entire Pandas DataFrame and break down each statistical measure. The “.describe()” method is a powerful tool for quickly analyzing data and extracting useful insights.

Understanding each statistical measure enables us to gain a deeper understanding of the data and make informed decisions. Finally, Pandas offers a suite of additional functions and tools for data manipulation, which can be useful for complex data analysis tasks.

Descriptive statistics are crucial for summarizing, analyzing, and visualizing data and for gaining insights into underlying patterns in data. In a Pandas DataFrame, we can use the “.describe()” method to obtain descriptive statistics for the entire DataFrame and the statistical measures such as count, mean, standard deviation, minimum value, 25th percentile, median, 75th percentile, and maximum value for each numerical column.

In addition, we can use “astype” method for integer conversion and “value_counts()” method for categorical data. Understanding descriptive statistics enables us to make informed decisions and actionable insights.

In conclusion, knowing how to get descriptive statistics for a Pandas DataFrame is essential for successful data analysis, and the methods discussed in this article can assist in the development of said solutions.