# Pandas Descriptive Statistics: Using Default Custom and No Percentiles

## Pandas describe() Function

Pandas is a popular library used for data manipulation and analysis in Python. It provides a wide range of features that make it easy to work with large and complex datasets.

One of the most useful features of pandas is the describe() function, which provides a summary of the dataset that includes statistics like count, mean, and standard deviation. In this article, we will discuss different ways to use the describe() function and explore some examples of how to create and view a DataFrame.

### Default Percentiles

The describe() function in pandas is incredibly useful as it provides a summary of the dataset’s main descriptive statistics, including mean, standard deviation, minimum and maximum values, and the percentiles. By default, pandas displays percentiles at 25%, 50%, and 75%, but this can be customized to show additional percentiles.

Percentiles represent the value below which a given percentage of observations in the data fall. For instance, the 25th percentile represents the value below which 25% of the observations in the data fall.

The syntax to use the pandas describe() function with default percentiles is as follows:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10]})
df.describe()

### Custom Percentiles

You can also customize the percentiles used by the describe() function by specifying a list of values between 0 and 1. For example, if you want to see percentiles at 10%, 50%, and 90%, you can use the following syntax:

df.describe(percentiles=[.1, .5, .9])

### No Percentiles

If you want to exclude percentiles from the summary statistics of the DataFrame, you can use the following syntax:

df.describe(percentiles=[])

## DataFrame Example

### Data Creation

Pandas DataFrame is a two-dimensional labeled data structure with columns of potentially different data types. You can create a DataFrame using various methods.

One of the simplest ways is to create a DataFrame from a dictionary. Consider the following example:

import pandas as pd
data = {'name': ['John', 'Sara', 'Peter', 'Mary'],
'age': [25, 34, 29, 41],
'gender': ['M', 'F', 'M', 'F']}
df = pd.DataFrame(data)

The code snippet above creates a dictionary with three keys: name, age, and gender. The values of these keys are lists of values.

The pandas DataFrame is created by passing the dictionary to the pd.DataFrame() function. The result is a DataFrame with three columns and four rows.

### Data Viewing

Once you have created a DataFrame, you may want to view the data. You can use the head() function to view the first few rows of the DataFrame or the tail() function to view the last few rows.

By default, these functions display the first or last five rows, but you can specify the number of rows to display by passing a parameter to the function. To view the first three rows of the DataFrame created above, you can use the following syntax:

### The output of the code snippet above will show the first three rows of the DataFrame df as follows:

To view the last two rows of the DataFrame created above, you can use the following syntax:

df.tail(2)

## Conclusion

Pandas is a powerful library that provides many functions to work with data in a structured and organized way. The describe() function is one of the most useful functions in pandas as it provides a quick summary of the main descriptive statistics of a DataFrame.

Creating a DataFrame is simple and straightforward; you can create a DataFrame from a dictionary or other data sources. Once you have created a DataFrame, you can use functions like head() or tail() to view the data.

I hope this article has provided you with valuable insights into the power of pandas.

## Descriptive Statistics in pandas

Pandas is a powerful library for data analysis in Python. It provides many useful functions that make it easier to calculate basic descriptive statistics for a dataset.

Descriptive statistics summarize and describe the main characteristics of a dataset. In this article, we will explore how to use pandas to calculate descriptive statistics for numeric variables, the metrics calculated by the describe() function and the importance of percentiles.

### Numeric Variables

Descriptive statistics are generally calculated for numeric variables, which include continuous and discrete values such as age, height, weight, income, and so on. In pandas, numeric variables are represented by the float and integer data types.

You can check if a column contains numeric values by using the dtype attribute of a pandas Series, which provides the data type of the values in the column.

import pandas as pd
print(df['age'].dtype)

The above code snippet retrieves the data type of the column called ‘age’ in the DataFrame df. If the data type is int or float, the column contains numeric data.

### Metrics Calculated by describe()

The describe() function calculates several metrics that summarize the main characteristics of a dataset, including:

• count: the number of non-missing values for a variable
• mean: the average value for a variable
• std: the standard deviation of a variable
• min: the minimum value of a variable
• 25%, 50%, and 75%: the percentiles of a variable
• max: the maximum value of a variable

Percentiles are particularly useful for understanding the distribution of a dataset. They divide the data distribution into equally sized portions, and the percentiles indicate the value below which a certain percentage of the observations fall.

To calculate the metrics for a DataFrame using the describe() function, you can simply call the function on the DataFrame:

import pandas as pd
summary = df.describe()
print(summary)

The above code retrieves the DataFrame from a CSV file and calculates the summary statistics using the describe() function. The resulting summary DataFrame will contain the minimum and maximum values, the mean, standard deviation, and percentile values for each numeric column in the original DataFrame.

### Importance of Percentiles

Percentiles are particularly useful for understanding how a dataset is distributed. They are often used to identify outliers or anomalies in a dataset that can skew results.

For example, the 25th and 75th percentiles indicate the range within which 50% of the observations fall. If the 75th percentile for a variable is much higher than the 25th percentile, this indicates that a few observations have extremely high values that may influence the overall distribution of the dataset.

Pandas automatically calculates the 25th, 50th, and 75th percentiles for each numeric column by default. However, you can also specify custom percentiles using the percentiles parameter of the describe() function.

For example, to calculate summary statistics for percentiles at 10%, 50%, and 90%, you can use the following code:

import pandas as pd
summary = df.describe(percentiles=[0.1, 0.5, 0.9])
print(summary)

### Example 1: Default Percentiles

To illustrate the use of the describe() function with default percentiles, let’s consider an example using a toy dataset. Suppose we have a dataset of student grades with the following variables: student_id, grade, and gender.

### Consider the following code for generating summary statistics of this dataset:

import pandas as pd
data = {'student_id': [1, 2, 3, 4, 5],
'grade': [78, 84, 92, 82, 88],
'gender': ['M', 'F', 'M', 'F', 'F']}
df = pd.DataFrame(data)
summary = df.describe()
print(summary)

The above code creates a dictionary with the data for the student grades dataset and converts it into a pandas DataFrame. Then, the describe() function is called to generate the summary statistics for the dataset.

The resulting summary DataFrame will include the count, mean, standard deviation, minimum and maximum, and percentiles for each numeric column in the dataset.

The summary statistics show that there are five students with grades ranging from 78 to 92.

The mean grade is 84.8, and the standard deviation is 4.06. The minimum and maximum grades are 78 and 92, respectively.

The percentiles show that 25% of the students have a grade below 82.75, 50% have a grade less than 84, and 75% have a grade below 88. In conclusion, the pandas library provides numerous functions that facilitate data manipulation and analysis, particularly in regards to descriptive statistics.

The describe() function is one of the most useful functions in pandas as it gives us a quick summary of the main descriptive statistics of a DataFrame. In addition, it is essential to understand the importance of percentiles regarding datasets distribution, as inaccurate interpretation can lead to improper conclusions.

Overall, pandas is an incredibly powerful and extremely versatile tool for anyone working with data in Python.

### Example 2: Custom Percentiles

In the previous example, we used the describe() function with default percentiles.

In some cases, however, the default percentiles may not provide sufficient information about the distribution of a dataset. In such cases, you can specify custom percentiles using the percentiles parameter of the describe() function.

Let’s consider an example using the same student grades dataset as before. Suppose we want to calculate summary statistics for percentiles at 20%, 40%, 60%, and 80%.

### We can modify the previous code to include custom percentiles as follows:

import pandas as pd
data = {'student_id': [1, 2, 3, 4, 5],
'grade': [78, 84, 92, 82, 88],
'gender': ['M', 'F', 'M', 'F', 'F']}
df = pd.DataFrame(data)
summary = df.describe(percentiles=[0.2, 0.4, 0.6, 0.8])
print(summary)

### The output of the code above will show the summary statistics with the custom percentiles as follows:

The summary statistics now give us a more detailed understanding of the distribution of the data. For example, we can see that 80% of the students have a grade below 88.4.

### Example 3: No Percentiles

In some cases, you may not be interested in the percentiles and may only want to view the count, mean, standard deviation, and other basic statistics for a dataset.

To exclude percentiles from the summary statistics, you can set the percentiles parameter to an empty list. Let’s consider an example using the same student grades dataset as before.

Suppose we want to calculate summary statistics but without percentiles. We can modify the previous code to exclude percentiles as follows:

import pandas as pd
data = {'student_id': [1, 2, 3, 4, 5],
'grade': [78, 84, 92, 82, 88],
'gender': ['M', 'F', 'M', 'F', 'F']}
df = pd.DataFrame(data)
summary = df.describe(percentiles=[])
print(summary)

### The output of the code above will show the summary statistics with no percentiles as follows:

The summary statistics now exclude percentiles but provide the mean, standard deviation, minimum, and maximum values of the dataset. In conclusion, the describe() function in pandas allows us to generate summary statistics for a dataset quickly.

You can customize the function to include or exclude percentiles and even specify custom percentiles to view additional information about a dataset’s distribution. Overall, pandas provides many useful tools for data analysis and is an essential library for any data science projects.Pandas is a powerful Python library for data analysis that provides numerous functions and tools to manipulate and summarize datasets.

The describe() function is one of the most useful features in pandas and allows us to generate summary statistics for a dataset quickly. In this article, we discussed different ways of using the describe() function, such as with default, custom, and no percentiles.

We also explored different examples of generating summary statistics for a dataset using the describe() function.

## Descriptive Statistics in Pandas

Descriptive statistics are used to analyze and summarize a set of data. In pandas, the describe() function can quickly generate a summary statistics table that includes important metrics such as count, mean, standard deviation, minimum, maximum, and percentiles.

Percentiles are an essential aspect of descriptive statistics as they help us to understand and interpret the distribution of a dataset. By default, the describe() function calculates percentiles at 25%, 50%, and 75%; however, we can customize these percentiles as per the requirements by setting the percentiles parameter.

### Numeric Variables

Descriptive statistics in pandas are generally calculated for numeric variables such as age, height, weight, and income. Numeric variables are represented by the float and integer data types in pandas.

We can determine whether a column contains numeric variables by using the dtype attribute of a pandas Series, which provides the data type of the values in the column.

### Metrics Calculated by describe()

The describe() function calculates different metrics to summarize a dataset, including count, mean, standard deviation, minimum, maximum, and percentiles. These metrics provide valuable insights into the distributions of the data and help us to identify trends and patterns.

The count metric indicates the number of non-missing values in a dataset, while the mean is the average of all values in a column. The standard deviation provides information about the spread of the data around the mean.

The minimum and maximum metrics indicate the smallest and largest values in a column, respectively.

### Example 1: Default Percentiles

In the first example, we discussed generating summary statistics of a student grade dataset with default percentiles.

The output of the code snippet demonstrated how the describe() function could quickly generate summary statistics for a dataset and provide insights into the distribution of data.

### Example 2: Custom Percentiles

In the second example, we discussed generating summary statistics of the same student grade dataset as before but with custom percentiles.

The output of the code snippet demonstrated how we can adjust the percentiles parameter to customize the output of the describe() function to our specific requirements.

### Example 3: No Percentiles

In the third example, we discussed generating summary statistics of the same student grade dataset as before but without percentiles.

The output of the code snippet demonstrated how we can exclude percentiles from the summary statistics if we are only interested in the mean, standard deviation, minimum, and maximum