When working with data we often would be required to concatenate two or multiple columns of text/string in pandas DataFrame, you can do this in several ways. In this article, I will cover the most used ways in my real-time projects to concatenate two or multiple columns of string/text type. While concat based on your need, you may be required to add a separator hence, I will explain examples with the separator as well.
1. Quick Examples of pandas Concatenate Two Columns of DataFrame
If you are in a hurry, below are some quick examples of how to concatenate two columns of text in pandas DataFrame.
# Below are quick example
# 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.
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. Concatenate Two Columns Using + Operator in pandas
By use + operator simply you can concatenate 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.
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 Concat Two String Columns
You can also use the DataFrame.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 a 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 agg() to Concat String Columns of DataFrame
To concat multiple string columns, you can also use DataFrame.agg() method. Like above pass all the columns, you wanted to concat 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 Concat Columns
By using series.str.cat() function you can concatenate two Series by a delimiter/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 to Concat
apply() method with lambda can be used to achieve the same. You can use this method to generalize 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. Concat Two Columns 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 of Concatenate Two Columns in pandas
Below is a complete example of how to concat two or multiple columns on pandas DataFrame.
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 concatenate 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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