# Mastering Frequency Count and DataFrame Display with Pandas

## Frequency Count and DataFrame Creation and Display in Pandas

Pandas is a popular data manipulation library in Python. It provides a vast array of tools and functions that help to streamline data analysis and processing.

In this article, we will discuss two important functions in the Pandas library: Frequency count and DataFrame creation and display.

### Method 1: Frequency Count in Table Format

One of the most common tasks in data analysis is obtaining the frequency counts of values in a given dataset.

Pandas provides the `value_counts()` function that we can use to achieve this. The function returns a table that displays the number of occurrences of each unique value in a column of a DataFrame.

Here is an example of how to use the `value_counts()` function:

``````import pandas as pd
# create a sample DataFrame
data = {'Column1': ['A', 'B', 'C', 'A', 'B', 'A'],
'Column2': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
# obtain the frequency count of values in Column1
freq_count = df['Column1'].value_counts()
# print the frequency count in table format
print(freq_count)
``````

### Output:

``````A    3
B    2
C    1
Name: Column1, dtype: int64
``````

As you can see, the `value_counts()` function returns a Series object that displays the frequency counts in descending order.

### Method 2: Frequency Count in Dictionary Format

In some scenarios, we may need to obtain the frequency counts of values in a DataFrame and store them in a dictionary format for further processing.

Pandas provides an easy way to achieve this using the `to_dict()` function. Here is an example of how to use the `to_dict()` function:

``````import pandas as pd
# create a sample DataFrame
data = {'Column1': ['A', 'B', 'C', 'A', 'B', 'A'],
'Column2': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
# obtain the frequency count of values in Column1
freq_count_dict = df['Column1'].value_counts().to_dict()
# print the frequency count in dictionary format
print(freq_count_dict)
``````

### Output:

``````{'A': 3, 'B': 2, 'C': 1}
``````

## DataFrame Creation and Display

Another crucial aspect of data analysis is creating a DataFrame and displaying it. Pandas provides a variety of functions that simplify this task.

### DataFrame Creation

To create a DataFrame, we need first to create a dictionary of the data we want to include in the DataFrame. The keys of the dictionary represent the column names, and the values represent the data for each column.

Then, we can use the `pd.DataFrame()` function to create the DataFrame. Here is an example of how to create a DataFrame using Pandas:

``````import pandas as pd
# create data dictionary
data = {'Column1': ['A', 'B', 'C', 'A', 'B', 'A'],
'Column2': [1, 2, 3, 4, 5, 6]}
# create DataFrame from dictionary
df = pd.DataFrame(data)
# print DataFrame
print(df)
``````

### Output:

``````  Column1  Column2
0       A        1
1       B        2
2       C        3
3       A        4
4       B        5
5       A        6
``````

### DataFrame Display

Once we have created a DataFrame, we may need to display it to the user. Pandas provides a variety of functions that allow us to do this.

The `print()` function is the most straightforward way to display a DataFrame in the console. However, it is not very visually appealing.

Pandas provides other functions that help to format the DataFrame better. For instance, the `head()` function displays the first n rows of the DataFrame, while the `tail()` function displays the last n rows.

Here is an example of how to use the `head()` function:

``````import pandas as pd
# create data dictionary
data = {'Column1': ['A', 'B', 'C', 'A', 'B', 'A'],
'Column2': [1, 2, 3, 4, 5, 6]}
# create DataFrame from dictionary
df = pd.DataFrame(data)
# display first 3 rows of DataFrame
``````

### Output:

``````  Column1  Column2
0       A        1
1       B        2
2       C        3
``````

## Conclusion

Frequency count and DataFrame creation and display are fundamental operations in data analysis. Pandas provides a variety of functions that simplify these tasks, making them easy to perform for even beginner data analysts.

By mastering these functions, you will have the groundwork to start working on more complex tasks in data analysis.