## Suppressing Scientific Notation in Pandas Describe()

Pandas is the most widely used open-source library for data manipulation and analysis in the Python programming language. It provides powerful data structures and tools that enable users to work with different data types, including numerical data.

However, when using the describe() method in Pandas, the output value might appear in Scientific Notation. This article will show you how to suppress Scientific Notation when using the describe() method in Pandas.

Method 1: Suppress Scientific Notation When Using describe() with One Column

The describe() method in Pandas generates descriptive statistics that summarize the central tendency, dispersion, and shape of a dataset. To suppress Scientific Notation in the output value when using describe() with one column, use the .apply() function with a lambda expression.

Here’s an example:

## import pandas as pd

## import numpy as np

# create a dataframe

df = pd.DataFrame({‘numbers’: [123456789, 987654321]})

# apply lambda function to suppress Scientific Notation

df[‘numbers’].apply(lambda x: ‘%.0f’ % x)

# use describe() method

df[‘numbers’].describe()

In this example, we first created a dataframe with two numbers in one column. Then, we applied a lambda function to the ‘numbers’ column to suppress Scientific Notation.

The lambda function ‘%.0f’ % x converts the values to a string with zero decimal places, removing Scientific Notation. Finally, we use the describe() method on the column to generate descriptive statistics without Scientific Notation.

Method 2: Suppress Scientific Notation When Using describe() with Multiple Columns

Suppose you have a dataset with multiple columns, and you want to suppress Scientific Notation in the output value when using the describe() method. In that case, you can modify the Pandas options to achieve this.

Here’s how:

## import pandas as pd

## import numpy as np

# create a dataframe

df = pd.DataFrame({‘numbers’: [123456789, 987654321], ‘more_numbers’: [123.456, 987.654]})

# modify Pandas options to suppress Scientific Notation

pd.options.display.float_format = ‘{:.2f}’.format

# use describe() method on dataframe

df.describe()

In this example, we first created a dataframe with two columns, ‘numbers’ and ‘more_numbers’. Then, we modified the Pandas options by setting the float_format option to ‘{:.2f}’.

This format option formats the floating-point numbers with two decimal places, removing Scientific Notation. Finally, we used the describe() method on the dataframe to generate descriptive statistics without Scientific Notation.

Example 1: Suppress Scientific Notation When Using describe() with One Column

Suppose you have a dataset with one column containing a large number that appears in Scientific Notation when using the describe() method. In that case, you can use the method described above to suppress Scientific Notation.

For example, suppose you have the following dataframe:

## import pandas as pd

## import numpy as np

df = pd.DataFrame({‘numbers’: [23456789012]})

When you use the describe() method on this dataframe:

df[‘numbers’].describe()

## The output will be:

count 1.00

mean 23456789012.00

## std nan

min 23456789012.00

25% 23456789012.00

50% 23456789012.00

75% 23456789012.00

max 23456789012.00

Name: numbers, dtype: float64

The mean value appears in Scientific Notation, making it difficult to read. To suppress Scientific Notation, apply the method described above:

df[‘numbers’].apply(lambda x: ‘%.0f’ % x)

df[‘numbers’].describe()

## The output will now be:

count 1.00

mean 23456789012.00

## std nan

min 23456789012.00

25% 23456789012.00

50% 23456789012.00

75% 23456789012.00

max 23456789012.00

Name: numbers, dtype: float64

The mean value is now easier to read, without Scientific Notation.

## Conclusion

Suppressing Scientific Notation in Pandas describe() can make the output values easier to read and understand. This article has shown you how to suppress Scientific Notation when using the describe() method in Pandas, whether you have one column or multiple columns in your dataset.

Applying these techniques ensures that your output values are precise and easy to understand, making your data manipulation and analysis more effective. In the previous section, we discussed two methods for suppressing Scientific Notation when using the describe() method in Pandas.

In this section, we will provide an example of using describe() with multiple columns and how to suppress Scientific Notation. We will also provide additional tutorials on using Pandas, a powerful library for data manipulation and analysis in Python.

Example 2: Suppress Scientific Notation When Using describe() with Multiple Columns

Suppose you have a dataset with multiple columns containing large numbers that appear in Scientific Notation when using the describe() method. In that case, you can use the method described in the previous section to suppress Scientific Notation.

For example, suppose you have the following dataframe:

## import pandas as pd

## import numpy as np

df = pd.DataFrame({‘numbers’: [23456789012, 98765432109], ‘more_numbers’: [123.456, 987.654]})

When you use the describe() method on this dataframe:

df.describe()

## The output will be:

numbers more_numbers

count 2.00 2.00

mean 61011110560.50 555.05

std 5248377097.49 572.10

min 23456789012.00 123.46

25% 42233949786.25 339.76

50% 61011110560.50 555.05

75% 79788271334.75 770.35

max 98565432109.00 987.65

Both ‘numbers’ columns’ mean, standard deviation, and quartiles appear in Scientific Notation, making it difficult to read. To suppress Scientific Notation in both columns, apply the method described in the previous section:

pd.options.display.float_format = ‘{:.0f}’.format

df.describe()

## The output will now be:

numbers more_numbers

## count 2 2

## mean 61011110561 555

## std 5248377097 572

## min 23456789012 123

25% 42233949786 340

50% 61011110561 555

75% 79788271335 770

## max 98565432109 988

Both ‘numbers’ columns’ mean, standard deviation, and quartiles now appear without Scientific Notation, making it much easier to read.

## Additional Tutorials

Pandas offers numerous features for data manipulation and analysis in Python; however, the full extent of these features can be overwhelming to beginners. Therefore, we provide some resources for additional learning on using Pandas.

1. Pandas Documentation: The official Pandas documentation is an exhaustive resource for learning about Pandas’ different aspects, including its various functions and methods.

It covers all topics, from data structures to data visualization, and provides extensive examples and use cases. 2.

Datacamp: Datacamp offers interactive courses on various data analysis tools, including Pandas. Their courses cover a wide range of topics, including data manipulation, visualization, data cleaning, and exploratory data analysis.

3. Pandas Cookbook: The Pandas Cookbook is a resource that provides concise examples of using Pandas to perform various data analysis tasks.

It covers a broad range of topics, from data cleaning and aggregation to time-series analysis and statistical modeling. 4.

YouTube Tutorials: YouTube offers numerous video tutorials on using Pandas for data analysis. These tutorials are beneficial for beginners who prefer visual tutorials over reading documents.

5. Kaggle: Kaggle hosts various datasets and competitions focusing on data science and machine learning.

It’s an excellent platform for learning Pandas as it provides practice datasets and questions for analysis. In conclusion, Pandas is an essential library for data manipulation and analysis in Python.

This article has shown how to suppress Scientific Notation when using the describe() method in Pandas, whether dealing with one or multiple columns. We have also provided additional resources for learning Pandas, allowing readers to further their knowledge and skills in data analysis.

In conclusion, the article has highlighted two methods for suppressing Scientific Notation when using the describe() method in Pandas. These methods are essential for generating descriptive statistics without the Scientific Notation, which can be difficult to read and understand.

Furthermore, we have provided an example of using describe() with multiple columns and how to suppress Scientific Notation. Lastly, we have suggested additional tutorials for learning Pandas, a powerful library for data manipulation and analysis in Python.

By applying these techniques and learning from the provided resources, readers can improve their data analysis skills and achieve better results. Overall, suppressing Scientific Notation in Pandas Describe() is a crucial technique for producing precise and meaningful output values.