
        
        
            
                
                
                    df = pd.DataFrame({"A":["a","b","a"]})
df
                
            
               A
0  a
1  b
2  a

Solution

To count the number of times the value "a" occurs in column A:


        
        
            
                
                
                    (df["A"] == "a").sum()
                
            
            2

Explanation

To break this down, we are first fetching a Series of booleans where True indicates a match:


        
        
            
                
                
                    (df["A"] == "a")
                
            
            0    True
1   False
2    True
Name: A, dtype: bool

Since the internal representation of a True is 1, and False is 0, we can simply take the sum of this Series to count the total occurrence:


        
        
            
                
                
                    (df["A"] == "a").sum()
                
            
            2

Counting occurrence of a single value in multiple columns

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":["a","b","a"],"B":["a","a","a"]})
df
                
            
                A   B
0   a   a
1   b   a
2   a   a

Solution

To get the number of "a" in each column:


        
        
            
                
                
                    (df == "a").sum()
                
            
            A    2
B    3
dtype: int64

The idea is the exact same as that of the single-column case above.

Counting occurrences of multiple values in a column

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":["a","b","a","c"]})
df
                
            
               A
0  a
1  b
2  a
3  c

Solution

To count the occurrences of multiple values in column A:


        
        
            
                
                
                    values = ["a","b"]
counts = df["A"].value_counts()
counts[values].sum()
                
            
            3

Explanation

We first obtain a frequency count of the values in column A using Series' value_counts():


        
        
            
                
                
                    counts = df["A"].value_counts()   # returns a Series
counts
                
            
            a    2
c    1
b    1
Name: A, dtype: int64

We then extract the values we are interested in using [] syntax:


        
        
            
                
                
                    counts[values]   # returns a Series
                
            
            a    2
b    1
Name: A, dtype: int64

We then use the Series' sum() method:


        
        
            
                
                
                    counts[values].sum()
                
            
            3

Counting the total number of occurrences

Consider the same df as above:


        
        
            
                
                
                    df = pd.DataFrame({"A":["a","b","a"],"B":["a","a","a"]})
df
                
            
                A   B
0   a   a
1   b   a
2   a   a

Solution

To count the total number of "a" in df:


        
        
            
                
                
                    (df == "a").sum().sum()
                
            
            5

Explanation

Once again, we first check for the presence of "a" like so:


        
        
            
                
                
                    df == "a"   # returns a Series
                
            
               A      B
0  True   True
1  False  True
2  True   True

True is internally represented as a 1, while False as a 0. Taking the sum of each column yields:


        
        
            
                
                
                    (df == "a").sum()   # returns a Series
                
            
            A    2
B    3
dtype: int64

This tells us that we have 2 occurrences of "a" in column A, and 3 in B. What we want is the total number so we must take a second sum:


        
        
            
                
                
                    (df == "a").sum().sum()
                
            
            5

Published by Isshin Inada

Edited by 0 others

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