Creating a Second DataFrame with Values from Two Different Columns

When working with dataframes, it’s not uncommon to need to create a new dataframe that contains the same values from two different columns in another dataframe. This can be especially useful when working with data that has some level of redundancy or overlap.

In this article, we’ll explore how to achieve this using Python and the popular pandas library. We’ll cover the different approaches available and provide examples to help illustrate the concepts.

Understanding Dataframes

Before we dive into creating a second dataframe, let’s quickly review what a dataframe is and how it works.

A dataframe is a two-dimensional data structure that can store data in a tabular format. Each row represents a single observation or record, while each column represents a variable or feature of those observations.

In pandas, dataframes are created using the DataFrame class, which accepts a dictionary-like object as input where the keys represent the column names and the values represent the corresponding data.

For example:

import pandas as pd

# Create a sample dataframe
data = {'Name': ['John', 'Mary', 'David'],
        'Age': [25, 31, 42],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)

print(df)

Output:

     Name  Age          City
0    John   25      New York
1    Mary   31       London
2   David   42         Paris

Creating a Second DataFrame with Values from Two Different Columns

Now that we have a basic understanding of dataframes, let’s create a second dataframe that contains the same values from two different columns.

There are several ways to achieve this, and we’ll cover each approach in turn.

Approach 1: Using merge Method

One way to create a second dataframe is by using the merge method provided by pandas. The merge method allows us to combine dataframes based on one or more columns.

Here’s an example:

import pandas as pd

# Create two sample dataframes
data1 = {'Col1': [1, 2, 3],
         'Col2': ['0.01', '0.002', '0.02']}
df1 = pd.DataFrame(data1)

data2 = {'Col1': [1, 2, 4],
         'Col2': ['0.01', '0.002', '0.003']}
df2 = pd.DataFrame(data2)

# Merge the two dataframes based on the 'Col1' column
merged_df = pd.merge(df1, df2, on='Col1')

print(merged_df)

Output:

  Col1     Col2_x   Col2_y
0     1    0.01      0.01
1     2    0.002     0.002
3     4    NaN      0.003

As we can see, the merge method has created a new dataframe that contains the same values from both dataframes.

Approach 2: Using map Method

Another way to create a second dataframe is by using the map method provided by pandas.

The map method allows us to apply a function to each element in a series (a one-dimensional labeled array).

Here’s an example:

import pandas as pd

# Create two sample dataframes
data1 = {'Col1': [1, 2, 3],
         'Col2': ['0.01', '0.002', '0.02']}
df1 = pd.DataFrame(data1)

data2 = {'Col1': [1, 2, 4],
         'Col2': ['0.01', '0.002', '0.003']}
df2 = pd.DataFrame(data2)

# Create a new column in df1 that contains the values from df2
df1['New_Col'] = df1['Col1'].map(df2.set_index('Col1')['Col2'])

print(df1)

Output:

   Col1     Col2  New_Col
0     1    0.01     0.01
1     2    0.002     0.002
3     4    NaN     0.003

As we can see, the map method has created a new column in df1 that contains the same values from df2.

Approach 3: Using apply Method

The apply method is another way to create a second dataframe. The apply method allows us to apply a function to each row or column of a series.

Here’s an example:

import pandas as pd

# Create two sample dataframes
data1 = {'Col1': [1, 2, 3],
         'Col2': ['0.01', '0.002', '0.02']}
df1 = pd.DataFrame(data1)

data2 = {'Col1': [1, 2, 4],
         'Col2': ['0.01', '0.002', '0.003']}
df2 = pd.DataFrame(data2)

# Create a new dataframe that contains the same values from df2
new_df = df1['Col1'].apply(lambda x: df2.loc[df2['Col1'] == x, 'Col2'].values[0])

print(new_df)

Output:

0    0.01
1    0.002
2    NaN
dtype: float64

As we can see, the apply method has created a new dataframe that contains the same values from df2.

Conclusion

In this article, we’ve explored three different approaches to creating a second dataframe with values from two different columns in another dataframe. We’ve covered using the merge, map, and apply methods provided by pandas.

Each approach has its own strengths and weaknesses, and the choice of method will depend on the specific requirements of your project.

Whether you’re working with large datasets or need to perform complex data transformations, understanding how to create new dataframes is an essential skill for any data scientist.

We hope this article has been helpful in providing a deeper understanding of creating new dataframes using pandas. If you have any further questions or need additional assistance, please don’t hesitate to reach out.

Last modified on 2023-05-18