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But that’s really foolish, because when you use axis-1 for an operation, such as .count(axis = 1), it actually computes the row counts.

Note that you can also do the same thing if you set axis = 'columns'. axis = 'columns' is the same as axis = 1. Having said that, I strongly discourage this notation, because it’s extremely confusing. Setting axis = 'columns' actually gives you the number of non-missing values for the rows. There Also for COUNTIF (similar to the pandas equivalent of COUNTIFS), it suffices to sum over the condition while for SUMIF, we need to index the frame. df['COUNTIF'] = (df[['A', 'B']] > 1).sum(axis=1)For COUNTIFS, you can simply sum over the condition. For example, to compute =COUNTIFS(A2:A8,">0", B2:B8, "<3"), you can do: countifs = ((df['A']>1) & (df['B']<3)).sum() By default, the method will drop any missing values. It can often be useful to include these values. This can be done by passing in True into the dropna= parameter. # Including Missing Values in the value_counts Method

Similar to the example above, if we wanted to count the number of rows matching a particular condition, we could create a boolean mask for this. For multiple conditions e.g. COUNTIFS/SUMIFS, a convenient method is query because it's very fast for large frames (where performance actually matters) and you don't need to worry about parentheses, bitwise-and etc. For example, to compute =SUMIFS(C2:C8, A2:A8,">1", B2:B8, "<3"), you can use df.query("A>1 and B<3")['C'].sum() But if you set numeric_only = True, the count method will return the counts for the numeric variables only (integers, floats, etc). To call the count method with a dataframe, you simply type the name of the dataframe, and then .count().One final comment on the axis parameter: to understand this parameter, you really need to understand axes. For an explanation of how axes work, you should read our tutorial on Numpy axes (Numpy axes are very similar to dataframe axes). numeric_only (optional) The above output indicates that there are 18 values in the Level column, and only 17 in the Students column. This, really, counts the number of values, rather than the number of rows. Number of Rows Containing a Value in a Pandas Dataframe