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The syntax of the count method is fairly simple, but there are a few ways to use it and a few parameters that can modify its functionality.

In terms of syntax, notice that we needed to set axis = 1 to count the number of missing values in the rows. Understanding Pandas “axes” is difficult, but it would definitely help if you reviewed Numpy axes. Pandas axes are essentially the same as axes for a 2D Numpy array. level (nt or str, optional): If the axis is a MultiIndex, count along a particular level, collapsing into a DataFrame. A str specifies the level name.You can try with: In [1]: s = pd.DataFrame('a'=[1,2,5, np.nan, np.nan,3],'b'=[1,3, np.nan, np.nan,3,np.nan])

As in the examples above, we’ll filter down to the capacity series where the values are greater than 60. We then apply value_counts and we get a count of each unique record within that dataframe. df['capacity'][df['capacity'] >= 60].value_counts()

The main thing to remember with the value_counts function is not to run this function on a column with too many, or all, unique values or it may be a little useless to view. Using describe() describe(self: ~FrameOrSeries, percentiles=None, include=None, exclude=None) If you’re looking for a bit more detail than just the count of records within your dataframe or series, use the describe()function and you can get additional information such as the mean, standard deviation, min, max, 25%, 50% and 75% thresholds within the data that is either a float or integer datatype column. You will not be getting counts of records that are any other datatypes, for that you may want to try some of the other functions. df.describe() As you can see from the above, not all the columns are showning counts, or any other values, in the output. To identify which columns you’ll be getting statistics on, you can use the df.dtypes function. df.dtypes I’ll explain exactly what the technique does, how the syntax works, and I’ll show you step-by-step examples so you can see Pandas count in action. You can also use an alternative notation, with axis = "columns" or axis = "rows". I strongly discourage you from using this notation, because it’s highly confusing. I explain why in the FAQ section.