How do I get the row count of a Pandas DataFrame?

Detailed row count tricks

Counting non-null columns

When your data may have missing values, and you need to count rows with non-null values in a single column, prefer:

non_null_rows = df['column'].count()
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Non-null row count per column

To count non-null rows for each column, try df.count(). You get a series back with counts like magic:

col_counts = df.count()
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Performance: Speed is key

When you're dealing with big data, performance matters. In such cases, df.shape[0] or len(df) are faster than len(df.index). These are constant time operations—like Flash, they're super fast regardless of DataFrame size!

Your Swiss army knife: Advanced pandas functions

Group-wise row counts

Use df.groupby('column_name').size() or df.groupby('column_name').count() to get row counts per group:

group_sizes = df.groupby('column_name').size()  #Group photo time! Cheese!
group_counts = df.groupby('column_name').count()  # Attendance check for the groups
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The Perfplot show: Visualizing speed differences

To understand the performance differences between these methods, plot them with Perfplot:

import pandas as pd
import perfplot

def setup(n):
    return pd.DataFrame({'A': range(n)})

perfplot.show(
    setup=setup,
    kernels=[
        lambda df: len(df),
        lambda df: df.shape[0],
        lambda df: len(df.index),
    ],
    labels=["len(df)", "df.shape[0]", "len(df.index)"],
    n_range=[2**k for k in range(20)],
    xlabel="Number of rows"
)  # It's race time - no snacking allowed!
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This renders a plot with the execution time for varying numbers of rows.

Counting techniques: With a pinch of creativity

Counting via indexes

You can count rows & columns using their respective indexes – that's Jedi level:

rows = len(df.index)  # Jedi move!
columns = len(df.columns)  # Another Jedi move!
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Counting in a Series

To deal with a Pandas Series, use:

serie_len = len(s)  # Keeping it simple sweetie (KISS)
serie_len = s.size  # What's your size buddy?
serie_len = len(s.index)  # Direct approach!
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Specific counts for grouped data

To count non-null rows for a specific group within a column, try:

group_counts = df.groupby('column_name')['other_column'].count()  # A group specific roll call
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A Perfplot snapshot!

Imagine Perfplot as a stopwatch timing different athletes (methods) in a race up the building's staircase. len(df) usually gets the gold medal!🏅

Applauding simplicity

Embrace simple methods like len(df) - everyone gets them, and pandas perform them quickly. It's like taking attendance at a meeting - easy and straightforward.🧮

Balance it like an acrobat

Choose the right tools for the job. len(df) for speed; df.count() to tackle missing data. It's about perfect balance. But don't fall!