Creating a Pandas DataFrame from a List of Items with Parsing and Matching

In this article, we’ll explore how to create a Pandas DataFrame from a list of items that require parsing and matching. We’ll go through the steps of defining a function to convert each tuple into a pandas Series, handling embedded spaces in country names, and dealing with countries without codes.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its core features is the ability to create DataFrames, which are two-dimensional tables that can be used to store and manipulate data. In this article, we’ll show how to create a DataFrame from a list of items that contain embedded spaces and require parsing and matching.

The Challenge

The problem statement provides us with a list of tuples, where each tuple contains a country name and a code (A, N, or Y). Some countries have embedded spaces in their names, and Guatemala has no code. We need to write a function that can convert each tuple into a pandas Series, handling these challenges.

Defining the Function

Our first step is to define a function called raw_data_to_series that takes an input list of tuples as an argument. The function will iterate through each tuple, extract the country name and code, and create a dictionary with the parsed data.

def raw_data_to_series(xs):
    """
    Convert a tuple into a pandas Series.

    Args:
        xs (tuple): A tuple containing a country name and a code.

    Returns:
        pd.Series: A pandas Series with the parsed data.
    """
    name, values = xs

    if values == 'No Data':
        return pd.Series(dtype='object').rename(name)

    values = values.replace('  ', ' ').split(' ')

    country = ''
    results = dict()

    for x in values:
        if x == 'GUATEMALA':
            results[x] = '?'
            country = ''
        elif country == '':
            country = x
        elif x in {'A', 'N', 'Y'}:
            results[country] = x
            country = ''
        else:
            country = country + ' ' + x

    return pd.Series(results).rename(name)

Applying the Function to the List of Tuples

Next, we need to apply the raw_data_to_series function to each tuple in the list of tuples. We can use a list comprehension to achieve this.

res = [('AFGHANISTAN', 'Y'), ('ARGENTINA', 'Y'), ('AUSTRALIA', 'Y'), ...]
dfte = pd.concat([raw_data_to_series(r) for r in res], axis=1)

The Resulting DataFrame

After applying the function to each tuple, we can see that our resulting DataFrame has the country names and codes as its columns.

                 63(I)[PARA.8] 63(I)[PARA.7] 63(I)[PARA.6] 99(I) 50(I)
AFGHANISTAN                  Y             Y             Y   NaN   NaN
ARGENTINA                    Y             Y             Y   NaN   NaN
AUSTRALIA                    Y             Y             Y   NaN   NaN
BELGIUM                      Y             Y             Y   NaN   NaN
BOLIVIA                      Y             Y             Y   NaN   NaN
BRAZIL                       N             N             N   NaN   NaN
BYELORUSSIAN SSR             Y             Y             Y   NaN   NaN
CANADA                       Y             Y             Y   NaN   NaN
CHILE                        Y             Y             Y   NaN   NaN
CHINA                        A             A             A   NaN   NaN

Conclusion

In this article, we demonstrated how to create a Pandas DataFrame from a list of items that require parsing and matching. We defined a function to convert each tuple into a pandas Series, handled embedded spaces in country names, and dealt with countries without codes. With these skills, you can tackle more complex data manipulation tasks in Python.

Last modified on 2024-06-02