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Pandas Get The Number of Rows in A DataFrame

First of all, have a look at this DataFrame, which we will use as a referential point for the rest of our guidelines. You may copy them into a text editor for better editing and comprehension.

DataFrame – Code: 

import pandas as pd

data = {
    'Level': ['Beginners', 'Intermediates ', 'Advanced', 'Beginners', 'Intermediates ', 'Advanced', 
        'Beginners', 'Intermediates ', 'Advanced', 'Beginners', 'Intermediates ', 'Advanced', 'Beginners', 
        'Intermediates ', 'Advanced', 'Beginners', 'Intermediates ', 'Advanced'], 
    'Pupils': [10, 20, 10, 40, 20, 10, None, 20, 20, 40, 10, 30, 30, 10, 10, 10, 40, 20]
    }

df = pd.DataFrame.from_dict(data)
print(df.head())

The codes above return this DataFrame:

DataFrame – Output:

         Level      Pupils
0      Beginners      10
1  Intermediates      20
2      Advanced       10
3      Beginners      40
4  Intermediates      20

Now, we will use this DataFrame to analyze the five methods introduced below.

Method 1. Use Pandas Len()

This function helps return the DataFrame length. Indeed, the safest and quickest way to identify how many rows there are in one DataFrame is to measure the lengths of those DataFrame’s indexes. 

Write these following codes to return the index’s length:

>> print(len(df.index))
18

Method 2. Use The “.Shape” Attribute

We may count on this attribute to send back a tuple containing the exact number of columns and rows in this format (rows; columns).

But what if you only care about the number of the rows? Then one solution is to get the tuple’s first index.

>> print(df.shape[0])
18

Method 3. Use The Count Method

This method, unfortunately, is among the slowest methods on this list. While the lens() and the “.shape” are relatively fast regardless of the DataFrame’s size, count() takes much longer for bigger DataFrames. One of its advantages, though, is the fact that missing values can be skipped.

>> print(df.count())
Level       18
Pupils    17
dtype: int64

Method 4. Use Boolean Masks

Suppose you need to count rows that contain one value. In such cases, we suggest you adopt boolean masks to the column and observe what rows match that condition.

Also, as Pandas treat “False” as 0 and “True” as 1, adding up the array is the quickest method.

Here is an example in which we count all the rows whose “Level” columns equate to “Beginners: 

>> print(sum(df['Level'] == 'Beginners'))
6

Similarly, if you want to count rows that match one specific condition, boolean masks will be your best bet. In the illustration below, we will count rows whose “Pupils” columns are not below 20:

>> print(sum(df['Pupils'] >= 20))
10

Method 5. Use The Groupby Method

We want to count rows in each category or group in several cases. Here is where the “.groupby()” command can be of help. This attribute sends back numerous row counts for each group.

print(df.groupby(['Level']).size())

We receive this series:

Level
Advanced         6
Beginners        6
Intermediates    6
dtype: int64

What Is “Pandas”?

“Pandas” has always been an open-source and famous Python package. “Pandas” is popular in data/data science analysis and AI learning tasks, established on the foundation of another package called “Numpy” (which assists multi-dimensional arrays)

Python programmers regard “Pandas” as among the most common data-wrangling packages. That is why Pandas can suit a lot of data science modules within the Python system.

You can find it included in almost every distribution in Python – from the ones coming with the operating system to industrial vendor distributions (such as the ActivePython from ActiveState).

What Is “DataFrame” in Pandas?

So what is DataFrame? It is a 2D (2-dimensional) monikered data structure, which features columns of different types. Let’s compare it to a SQL table, a spreadsheet, or a dict containing Series objects. “Pandas” has a lot of objects, but DataFrame remains the most popular. It accepts numerous input types, such as:

• Dict of Series, dicts, lists, and ndarrays (all are 1D)
• 2D numpy.ndarray
• Record or structured ndarray
• Series
• Another DataFrame

Another optional approach is to pass columns (or column labels) and index (or row labels) notions with the data. You will get the columns and/or the index of an output DataFrame. Hence, a specific index plus a Series dict will remove all unmatched data that does not fit your passed index.

And what if the system does not pass all axis labels? Then these labels will stem from your input data (depending on the rules of common senses).

The syntax for DataFrame creation is:

Syntax:

pandas.DataFrame(data, index, columns)

in which:

• Data: data represents a dataset which your Dataframe stems from. You can make it into ndarrays, series, scalar values, dictionaries, lists, etc. 
• Index: This function is optional, which starts from zero by default and finishes at the final data value (n-1) to give your row label and explicit definition.
• Columns: People employ the Column parameters to offer column names within the DataFrame. Suppose your column name never receives any default definition; it will automatically pick up any value running from 0 to “n-1”.

Conclusion

This article has delivered tips on Pandas get the number of rows in a DataFrame. The process is fairly uncomplicated, so we hope that you might find this task a little bit easier with the example above!So another inquiry arises: is it possible to convert your dictionary into a DataFrame? Of course, it is a yes! Check our ITtutoria guidelines for more support. You can also browse other tutorials for other Pandas-related issues!