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

For a dataframe df, one can use any of the following:

• len(df.index)
• df.shape[0]
• df[df.columns[0]].count() (== number of non-NaN values in first column)

Code to reproduce the plot:

import numpy as np
import pandas as pd
import perfplot

perfplot.save(
    "out.png",
    setup=lambda n: pd.DataFrame(np.arange(n * 3).reshape(n, 3)),
    n_range=[2**k for k in range(25)],
    kernels=[
        lambda df: len(df.index),
        lambda df: df.shape[0],
        lambda df: df[df.columns[0]].count(),
    ],
    labels=["len(df.index)", "df.shape[0]", "df[df.columns[0]].count()"],
    xlabel="Number of rows",
)

Suppose df is your dataframe then:

count_row = df.shape[0]  # Gives number of rows
count_col = df.shape[1]  # Gives number of columns

Or, more succinctly,

r, c = df.shape

Use len(df) :-).

__len__() is documented with "Returns length of index".

Timing info, set up the same way as in root's answer:

In [7]: timeit len(df.index)
1000000 loops, best of 3: 248 ns per loop

In [8]: timeit len(df)
1000000 loops, best of 3: 573 ns per loop

Due to one additional function call, it is of course correct to say that it is a bit slower than calling len(df.index) directly. But this should not matter in most cases. I find len(df) to be quite readable.

TL;DR use len(df)

len() returns the number of items(the length) of a list object(also works for dictionary, string, tuple or range objects). So, for getting row counts of a DataFrame, simply use len(df). For more about len function, see the official page.

Alternatively, you can access all rows and all columns with df.index, and df.columns,respectively. Since you can use the len(anyList) for getting the element numbers, using the len(df.index) will give the number of rows, and len(df.columns) will give the number of columns.

Or, you can use df.shape which returns the number of rows and columns together (as a tuple) where you can access each item with its index. If you want to access the number of rows, only use df.shape[0]. For the number of columns, only use: df.shape[1].