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() (slowest, but avoids counting NaN values in the first column)

Code to reproduce the plot:

import numpy as npimport pandas as pdimport perfplotperfplot.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 rowscount_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 loopIn [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.