## Counting Occurrences of True and False in Pandas DataFrame: A Beginner’s Guide

As a data analyst or scientist, it is essential to have a good understanding of the pandas library and its various functionalities. Pandas is a popular data manipulation tool, widely used in the scientific community, to work with tabular and heterogeneous data.

In this article, we will explore how to count occurrences of True and False values in a pandas DataFrame. We will cover the basic syntax, counting both True and False values, counting specific values, and a practical example of counting occurrences in pandas.

## Basic Syntax

Before we dive into counting True and False values in pandas, let’s take a quick look at the basic syntax used to create a pandas DataFrame. A pandas DataFrame is a 2-dimensional labeled data structure that can hold data of different types.

A DataFrame can be created from a list, dictionary, or even a CSV file. Here is an example of creating a pandas DataFrame from a dictionary:

```
import pandas as pd
data = {'Name': ['John', 'Emma', 'Alex', 'Kate'],
'Age': [25, 32, 19, 34],
'Employed': [True, True, False, False]}
df = pd.DataFrame(data)
print(df)
```

### Output:

```
Name Age Employed
0 John 25 True
1 Emma 32 True
2 Alex 19 False
3 Kate 34 False
```

## Counting both True and False values

Now that we have a pandas DataFrame to work with, let’s count the occurrences of True and False values in the ‘Employed’ column. The most common method used to count the occurrences of the values in a pandas DataFrame is the value_counts() method.

The value_counts() method returns a Series containing the counts of unique values in a pandas DataFrame. Here’s how you can use this method to count the number of True and False values in the ‘Employed’ column:

```
counts = df['Employed'].value_counts()
print(counts)
```

### Output:

```
True 2
False 2
Name: Employed, dtype: int64
```

From the output, we can see that there are two True values and two False values in the ‘Employed’ column. The method automatically sorted the counts in descending order.

## Counting specific values

Sometimes, we may want to count only specific values in a DataFrame. We can use the sum() method to count the occurrences of specific values in a particular column.

For example, let’s count the occurrences of True values in the ‘Employed’ column.

```
count_true = (df['Employed'] == True).sum()
print(count_true)
```

### Output:

```
2
```

From the output, we can see that there are two True values in the ‘Employed’ column.

## Practical Example: Counting Occurrences of True and False in Pandas

To better understand how to count occurrences in pandas, let’s consider the following example.

We have a list of basketball players who have played in the NBA. For each player, we have their name, height, weight, and whether they were born in the USA (True or False).

```
import pandas as pd
players = {'Name': ['LeBron James', 'Stephen Curry', 'Kevin Durant', 'Giannis Antetokounmpo', 'Luka Doncic'],
'Height (cm)': [203, 191, 211, 211, 201],
'Weight (kg)': [113, 86, 108, 109, 104],
'USA Born': [True, False, False, False, False]}
df = pd.DataFrame(players)
print(df)
```

### Output:

```
Name Height (cm) Weight (kg) USA Born
0 LeBron James 203 113 True
1 Stephen Curry 191 86 False
2 Kevin Durant 211 108 False
3 Giannis Antetokounmpo 211 109 False
4 Luka Doncic 201 104 False
```

Next, we will count the number of basketball players born in the USA.

```
count_usa = (df['USA Born'] == True).sum()
print(count_usa)
```

### Output:

```
1
```

From the output, we can see that there is only one basketball player born in the USA.

## Additional Resources

Pandas is a vast library that provides many functionalities for common data manipulation tasks. If you’re interested in learning more about pandas or want to explore other common tasks, here are a few resources that may be helpful:

- Pandas documentation: https://pandas.pydata.org/docs/
- Pandas Cheat Sheet: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf
- 10 Minutes to Pandas: https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html

## Conclusion

Counting occurrences of True and False values in a pandas DataFrame is a fundamental skill for data analysts and scientists. We hope that this beginner’s guide has been helpful in explaining the basic syntax, how to count both True and False values, how to count specific values, and a practical example of using pandas for counting occurrences.

Remember, pandas can be a powerful tool for data manipulation, and there are many resources available to continue learning and exploring this popular library. In summary, this article serves as a beginner’s guide to counting occurrences of True and False values in a pandas DataFrame.

We covered the basic syntax of creating a pandas DataFrame, how to count both True and False values using the value_counts() method, and how to count specific values using the sum() method. We also provided a practical example of using pandas for counting occurrences.

This skill is crucial for data analysts and scientists, and with the resources provided, readers can continue to improve their understanding and mastery of pandas. In conclusion, mastering pandas can be a powerful tool for data manipulation, and by understanding how to count occurrences, analysts can gain deeper insights into their datasets.