Pandas Combine Two Columns of Text in DataFrame

When working with data we often would be required to combine/merge two or multiple columns of text/string in pandas DataFrame, you can do this in several ways. In this article, I will cover mostly used ways in my real-time projects to combine/merge multiple string/text columns. While merging based on your need, you may be required to add a separator hence, I will explain examples with the separator.

1. Quick Examples of Combine Two Columns of Text

If you are in a hurry, below are some quick examples of how to combine two columns of text in pandas DataFrame.

# Below are some quick examples

# Using + operator to combine two columns
df["Period"] = df['Courses'].astype(str) +"-"+ df["Duration"]

# Using apply() method to combine two columns of text
df["Period"] = df[["Courses", "Duration"]].apply("-".join, axis=1)

# Using DataFrame.agg() to combine two columns of text
df["period"] = df[['Courses', 'Duration']].agg('-'.join, axis=1)

# Using Series.str.cat() function
df["Period"] = df["Courses"].str.cat(df["Duration"], sep="-")

# Using DataFrame.apply() and lambda function
df["Period"] = df[["Courses", "Duration"]].apply(lambda x: "-".join(x), axis =1)

# Using map() function to combine two columns of text
df["Period"] = df["Courses"].map(str) + "-" + df["Duration"]

Now, let’s run these examples by creating a DataFrame. Our DataFrame contains column names Courses, Fee, Duration, and Discount, I will merge columns Courses & Duration with ‘-‘ separator and creates a new column Period.

import pandas as pd
technologies = ({
     'Courses':["Spark","PySpark","Hadoop","Python","pandas"],
     'Fee' :[20000,25000,26000,22000,24000],
     'Duration':['30days','40days','35days','40days','60days'],
     'Discount':[1000,1500,2500,2100,2000]
               })
df = pd.DataFrame(technologies)
print(df)

Yields below output.

# Output:
   Courses    Fee Duration  Discount
0    Spark  20000   30days      1000
1  PySpark  25000   40days      1500
2   Hadoop  26000   35days      2500
3   Python  22000   40days      2100
4   pandas  24000   60days      2000

2. Combine Two Columns Using + Operator

By use + operator simply you can combine/merge two or multiple text/string columns in pandas DataFrame. Note that when you apply + operator on numeric columns it actually does addition instead of concatenation.

# Using + operator to combine two columns
df["Period"] = df['Courses'].astype(str) +"-"+ df["Duration"]
print(df)

Yields below output.

# Output:
   Courses    Fee Duration  Discount          Period
0    Spark  20000   30days      1000    Spark-30days
1  PySpark  25000   40days      1500  PySpark-40days
2   Hadoop  26000   35days      2500   Hadoop-35days
3   Python  22000   40days      2100   Python-40days
4   pandas  24000   60days      2000   pandas-60days

3. Using .apply() Method to Combine Two String Columns

You can also use the .apply() function compressing two or multiple columns of the DataFrame to a single column. join() function is used to join strings. DataFrame.apply() function is used to apply another function on a specific axis.

# Using apply() method to combine two columns of text
df["Period"] = df[["Courses", "Duration"]].apply("-".join, axis=1)
print(df)

Yields same output as above.

4. Using DataFrame.agg() Method to Merge String Columns

To join multiple string columns, you can also use DataFrame.agg() method. Like above pass all the columns you wanted to merge as a list.

# Using DataFrame.agg() to combine two columns of text
df["period"] = df[['Courses', 'Duration']].agg('-'.join, axis=1)
print(df)

Yields same output as above.

5. Using Series.str.cat() Function to Combine Two Columns of Text

By using series.str.cat() function you can combine two Series by a separator. You can apply this with DataFrame as below. Here df["courses"] & df["Duration"] returns series.

# Using Series.str.cat() function 
df["Period"] = df["Courses"].str.cat(df["Duration"], sep = "-")
print(df)

Yields same output as above.

6. Using DataFrame.apply() and Lambda Function

apply() method with lambda can be used to achieve the same. You can use this method generalizes to an arbitrary number of string columns by replacing df[["Courses", "Duration"]] with any column slice of your DataFrame.

# Using DataFrame.apply() and lambda function
df["Period"] = df[["Courses", "Duration"]].apply(lambda x: " ".join(x), axis =1)
print(df)

Yields same output as above.

7. Combine Two Columns of Text Using map() Function

Finally, map() is also used to concatenate multiple columns. Using map() you get more freedom even to check conditions.

# Using map() function to combine two columns of text
df["Period"] = df["Courses"].map(str) + " " + df["Duration"]
print(df)

Yields same output as above.

8. Complete Example For Combine Two Columns of Text

import pandas as pd
technologies = ({
     'Courses':["Spark","PySpark","Hadoop","Python","pandas"],
     'Fee' :[20000,25000,26000,22000,24000],
     'Duration':['30days','40days','35days','40days','60days'],
     'Discount':[1000,1500,2500,2100,2000]
               })
df = pd.DataFrame(technologies)
print(df)

# Using + operator to combine two columns
df["Period"] = df['Courses'].astype(str) +"-"+ df["Duration"]
print(df)

# Using apply() method to combine two columns of text
df["Period"] = df[["Courses", "Duration"]].apply("-".join, axis=1)
print(df)

# Using DataFrame.agg() to combine two columns of text
df["period"] = df[['Courses', 'Duration']].agg('-'.join, axis=1)
print(df)

# Using Series.str.cat() function
df["Period"] = df["Courses"].str.cat(df["Duration"], sep = "-")
print(df)

# Using DataFrame.apply() and lambda function
df["Period"] = df[["Courses", "Duration"]].apply(lambda x: "-".join(x), axis =1)
print(df)

# Using map() function to combine two columns of text
df["Period"] = df["Courses"].map(str) + "-" + df["Duration"]
print(df)

Conclusion

In this article, you have learned how to combine two or multiple string columns in pandas DataFrame using + operator, DataFrame.map(), DataFrame.agg(), and Series.str.cat(), DataFrame.apply() method.

Happy Learning !!

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References

• https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.combine.html