Pandas: Setting no. of max rows

🐼Pandas: Setting no. of max rows 📊

Are you a data enthusiast, using Pandas in your data analysis? 🤔

Have you ever encountered a situation where your DataFrame in Pandas doesn't display all the rows by default? 📉😱

Worry not! In this blog post, we will address exactly this common issue and provide you with easy solutions to display the entire array in Pandas. 🚀📊

Let's jump right in 👇

The Problem 🧩

Imagine you have a DataFrame and you want to view all the rows, but by default, Pandas limits the number of rows it displays. 🤨

Here's an example of such a DataFrame:

n = 100
foo = DataFrame(index=range(n))
foo['floats'] = np.random.randn(n)
foo

The output might look something like this:

floats
0    1.234
1    2.345
2    0.123
.
.
.
98   4.567
99   0.987

In this case, Pandas only displays a limited number of rows, and you have to slice the DataFrame to view the remaining rows. 😫

The Solution 🛠️

To overcome this issue, you can use the pd.set_option('display.max_rows', None) statement to display the entire DataFrame. 🙌

This sets the maximum number of rows to be displayed as None, which means Pandas will display all rows.

Here's an example of how to implement this solution:

import pandas as pd

pd.set_option('display.max_rows', None)

n = 100
foo = DataFrame(index=range(n))
foo['floats'] = np.random.randn(n)
foo

After implementing this solution, the output will display all the rows of the DataFrame:

floats
0    1.234
1    2.345
2    0.123
.
.
.
97   3.210
98   4.567
99   0.987

Now you can easily view and analyze your complete DataFrame without any limitations. 😎📊

Conclusion 📝

By using the pd.set_option('display.max_rows', None) statement, you can overcome the default limitation of displaying a limited number of rows in a Pandas DataFrame. This simple solution enables you to view and analyze the entire array effortlessly. 💪📊

So, next time you encounter this issue, remember this handy solution and dive into your data analysis without constraints! 🚀

If you found this blog post helpful, feel free to share it with your fellow data enthusiasts. And if you have any more questions or tips to share, leave a comment below! Let's exchange our knowledge and make data analysis even better together! 🤝🌟

Happy pandas-ing! 🐼🐾