how to merge three columns of data into one dataframe

To merge three columns of data into one DataFrame, you can use the `pd.concat()` function from the pandas library. Here's an example:

import pandas as pd

# Example data
data1 = [1, 2, 3, 4, 5]
data2 = [6, 7, 8, 9, 10]
data3 = [11, 12, 13, 14, 15]

# Create separate Series for each column
column1 = pd.Series(data1, name='Column1')
column2 = pd.Series(data2, name='Column2')
column3 = pd.Series(data3, name='Column3')

# Concatenate the Series into a single DataFrame
df = pd.concat([column1, column2, column3], axis=1)

# Print the resulting DataFrame
print(df)

In the code above, we start by importing the pandas library. Then, we create three separate lists (`data1`, `data2`, `data3`) representing the data for each column.

Next, we create individual Series objects for each column using the `pd.Series()` function. We provide the data for each column and specify the desired column name using the `name` parameter.

Finally, we use the `pd.concat()` function to concatenate the Series objects along the columns axis (`axis=1`). This creates a single DataFrame (`df`) with three columns. We then print the resulting DataFrame.

The output will be:

   Column1  Column2  Column3
0        1        6       11
1        2        7       12
2        3        8       13
3        4        9       14
4        5       10       15

This creates a DataFrame with three columns named "Column1", "Column2", and "Column3" containing the data from the original three columns.