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In Pandas, the `count()` function is used to count the number of non-null elements in a Series. It effectively provides a count of the observations or data points that have valid values, excluding any null values (NaN). This function is particularly useful for assessing data completeness and identifying missing or incomplete data in a dataset.

In this article, I will explain the `count()` function by using its syntax, parameters, usage and how we can return an integer representing the number of non-null elements in the Series. This count excludes any null values (NaN).

Key Points

• The `count()` function is used to count the number of non-NA/null entries in a Series.
• It returns an integer representing the number of non-null elements in the Series.
• Null values (NaN) are not counted when using the `count()` function.
• The function can also be used to count elements based on specific conditions using boolean indexing.
• The count of a Series can be useful in data preprocessing and cleaning tasks to identify missing or incomplete data.

## Pandas Series count() Introduction

Following is the syntax of the pandas Series count() function.

``````
# Syntax of series count()
Series.count()
``````

### Parameters of the Series count()

This function doesn’t take any parameters. It’s called directly on a Pandas Series object to count the number of non-null elements within that Series.

### Return Value

It returns an integer representing the count of non-null elements in the Series.

## Counting Non-Null Elements in a Series

To count the non-null elements in a Pandas Series, you can use the `count()` function directly on the Series object.

Now, let’s create a Pandas Series from a Python list.

``````
import pandas as pd

# Create Pandas Series
data = [2, 4, None, 6, None, 8]
series = pd.Series(data)
print("Original Series:\n",series)
``````

Output:

Here, you use the `count()` function on the `series` object to count the non-null elements. The `count()` function returns the number of non-null elements in the Series, excluding any `NaN` (Not a Number) values.

``````
# Count the non-null elements in the Series
count = series.count()
print("Number of non-null elements in the Series:", count)
``````

In the above example, the `count()` function returns 4 because there are 4 non-null elements in the Series. The null values (`None`) are not counted. This example yields the below output

Alternatively, to count the non-zero elements in a Pandas Series, you can use boolean indexing combined with the `sum()` function.

``````
import pandas as pd

# Create a sample Series
data = pd.Series([0, 1, 3, 5, 0, 7, 0])

# Count the non-zero elements in the Series
count_non_zero = (data != 0).sum()
print("Number of non-zero elements in the Series:", count_non_zero)

# Output:
# Number of non-zero elements in the Series: 4
``````

In the above example, the expression `(data != 0)` creates a boolean mask that `True` indicates non-zero values. Then, `sum()` is used to count the number of `True` values in the mask, corresponding to the number of non-zero elements in the Series.

## Counting String Occurrences

To count occurrences of specific string values in a Pandas Series, you can use boolean indexing to filter the elements equal to the target string and then apply the sum() function to count the occurrences.

``````
import pandas as pd

# Create a sample Series
data = pd.Series(['Spark', 'MongoDB', 'MongoDB', 'Spark', 'Pandas', 'MongoDB'])

# Count the occurrences of a specific string, e.g., 'MongoDB'
target_string = 'MongoDB'
count_occurrences = (data == target_string).sum()
print(f"Number of occurrences of '{target_string}': {count_occurrences}")

# Output:
# Number of occurrences of 'MongoDB': 3
``````

In the above example, the expression `(data == target_string)` creates a boolean mask where `True` indicates elements equal to `MongoDB`. Then, `sum()` is used to count the number of `True` values in the mask, which corresponds to the number of occurrences of the target string in the Series.

## Counting Unique Elements in a Series

Similarly, to count the number of unique elements in a Pandas Series, you can use the `nunique()` function. For instance, the `nunique()` function returns the count of unique elements in the Series, which is `3` in this case (`Spark``MongoDB`, and `Pandas`).

``````
import pandas as pd

# Create a sample Series
data = pd.Series(['Spark', 'MongoDB', 'MongoDB', 'Spark', 'Pandas', 'MongoDB'])

# Count the number of unique elements in the Series
count_unique = data.nunique()
print("Number of unique elements in the Series:", count_unique)

# Output:
# Number of unique elements in the Series: 3
``````

## Counting Elements in a Series Based on Index Labels

You can also count elements in a Pandas Series based on specific index labels, you can use the `loc[]` accessor along with the `count()` function.

``````
import pandas as pd

# Create a sample Series with custom index labels
data = pd.Series([10, 20, 30, 40, 50], index=['a', 'b', 'c', 'd', 'e'])

# Count elements with specific index labels
result = data.loc[['a', 'c', 'e']].count()
print("Number of elements with index labels 'a', 'c', and 'e':", result)

# Output:
# Number of elements with index labels 'a', 'c', and 'e': 3
``````

In the above example, the `loc[[a, c, e]]` is used to select elements with index labels `a``c`, and `e`. Then, the `count()` function is applied to count the number of non-null elements among these selected elements.

## Counting Boolean Values

Counting boolean values in a Pandas Series involves using the `sum()` function with boolean indexing or directly passing the boolean Series to the `sum()` function.

You can use boolean indexing to filter the Series based on the boolean condition and then apply the `sum()` function to count the `True` values.

``````
import pandas as pd

# Create a Series of boolean values
ser = pd.Series([True, False, True, True, False, True])

# Count True values using boolean indexing
count_true = ser[ser == True].count()
print("Count of True values:", count_true)

# Output:
# Count of True values: 4
``````

You can directly pass the boolean Series to the `sum()` function, which will interpret `True` as 1 and `False` as 0, effectively counting the `True` values.

``````
# Count True values using direct sum
count_true = ser.sum()
print("Count of True values:", count_true)

# Output:
# Count of True values: 4
``````

## FAQ on Pandas Series count() Function

What does the count() function do in Pandas Series?

The `count()` function in Pandas Series is used to count the number of non-null (non-NA) entries in the Series. This means it provides the count of valid data points, excluding any entries that are NaN (null) or missing. This function is particularly useful for quickly determining how many valid (non-missing) values are present in a dataset.

How does count() handle null values?

The `count()` function in Pandas Series handles null values (represented as NaN – Not a Number) by excluding them from the count. This means that when you call `count()` on a Pandas Series, it returns the number of non-null elements within the Series.

Can count() be used with mixed data types in a Series?

The `count()` function in Pandas Series can be used with mixed data types. It counts the number of non-null elements regardless of their data types.

How do I use count() on a DataFrame?

The `count()` function can also be used on a DataFrame, where it returns the count of non-null values for each column. For a Series, the syntax is `Series.count()`. For a DataFrame, the syntax is `DataFrame.count()`.

## Conclusion

In this article, I have explained the `count()` function in Pandas Series is a fundamental tool for assessing data completeness by providing the count of non-null elements within the Series. This function excludes null values (NaN), making it particularly useful for data preprocessing tasks, such as identifying and handling missing or incomplete data with examples.

Happy Learning!!

## References

• https://pandas.pydata.org/docs/reference/api/pandas.Series.count.html