Are you getting the “ValueError: columns must be same length as key” error in Python while working with pandas DataFrames?
The “ValueError: columns must be the same length as key” error is raised in Python when working with pandas DataFrame to convert a list into a column. The number of columns should be the same as the data columns; otherwise, you’ll get this ValueError.
In this article, we’ll discuss why we get the ValueError: column must be the same length as the key and how to fix it, along with practical examples. Furthermore, we’ll discuss converting a list into a DataFrame column in Python.
Let’s get straight into the topic and see how it works!
Table of Contents
What is the “ValueError: Columns Must be Same Length as Key” Error in Python?
We get this error when the number of columns doesn’t match the data column in Python. Let us say you have a list of students, and you want to convert that list into two columns, students and teacher, which isn’t valid to do. Because one list means one column, you cannot give two names to a single column.
For further understanding, let’s have a look at a practical example.
Code
import pandas as pd # create a list of students Students = ["James", "John", "Jenny", "Marry",] # convert the list a DataFrame column df1 = pd.DataFrame(Students, columns=['Students']) print(df2)
Output
Students 0 James 1 John 2 Jenny 3 Marry
Now let’s try to split the above column Students into two and see how it works:
Code
import pandas as pd # create a nested list Students = ["James", "John", "Jenny", "Marry"] # convert the list into DataFrame columns 'Students' and 'Teachers' df = pd.DataFrame(Students, columns=['Studnets', 'Teachers']) print(df)
Output
See, we are getting the ValueError: column must be the same length as the key, and the reason behind this is that we cannot represent a single column with two names in pandas DataFrame.
How to Fix the “ValueError: Column Must be Same Length as Key” Error in Python?
To fix the ValueError, we need to provide a valid number of columns to the data. Like a single list will represent a single column, whereas multiple lists will represent multiple columns of the same length.
Let’s see an example for further understanding:
Code
import pandas as pd # create a nested list Data = [["Albert", "Jasica"], ["Heisenberg", "Shane"]] # convert the nested list a DataFrame columns' Students' and 'Teachers' df = pd.DataFrame(Data, columns=['Students', 'Teachers']) print(df)
Output
Students Teachers 0 Albert Jasica 1 Heisenberg Shane
In the above example, we have created a nested list that can be represented in multiple columns in a DataFrame. The nested list has two lists, so we represent it in two columns.
Furthermore, we can also use a dictionary to convert it into a DataFrame column in Python. Let’s see an example of it:
Code
import pandas as pd
# create a data dictionary
dic = {'Students' : ["James", "John", "Jenny", "Marry"],
"Teachers" : ["Albert", "Jasica", "Tom", "Heisenberg"]}
# create a DataFrame
df = pd.DataFrame(dic,columns=['Students','Teachers'])
print("********** Data **********")
print(df)
Output
********** Data ********** Students Teachers 0 James Albert 1 John Jasica 2 Jenny Tom 3 Marry Heisenberg
In the above example, we’ve created a dictionary we two key-value pairs to each represent a column which is in the next step converted into a dictionary with the names of the columns as Students and Teacher.
Conclusion
To conclude the article on how to fix the “ValueError: columns must be the same length as key” error, we’ve discussed why it raises the ValueError and how to fix it along with practical examples. Furthermore, as a solution part, we’ve seen how to represent a nested list into a data frame with column names as labels.
To extend the solution of this ValueError, we’ve also discussed how to use a dictionary to convert it into a DataFrame and represent the data in columns with user-defined labels as column names.
Time to explore more 💡; Can we represent a nested dictionary into a data frame? If yes, please comment down the code for it.
