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Add new column in Pandas Data Frame Using a Dictionary
Pandas Data Frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. It can be created using python dict, list, and series etc. In this article, we will see how to add a new column to an existing data frame.
So first let's create a data frame using pandas series. In the below example we are converting a pandas series to a Data Frame of one column, giving it a column name Month_no.
Example
import pandas as pd s = pd.Series([6,8,3,1,12]) df = pd.DataFrame(s,columns=['Month_No']) print (df)
Output
Running the above code gives us the following result −
Month_No 0 6 1 8 2 3 3 1 4 12
Next we create a new python dictionary containing the month names with values from the pandas series as the indices of the dictionary. Then we use a map function to add the month's dictionary with the existing Data Frame to get a new column. The map function takes care of arranging the month names with the indices of the dictionary.
Example
import pandas as pd
s = pd.Series([6,8,3,1,12])
df = pd.DataFrame(s,columns=['Month_No'])
months = {6:'Jun', 8:'Aug', 3:'Mar', 1:'Jan',12:'Dec'}
df['Month_Name'] = df['Month_No'].map(months)
print (df)Output
Running the above code gives us the following result −
Month_No Month_Name 0 6 Jun 1 8 Aug 2 3 Mar 3 1 Jan 4 12 Dec
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