JIYIK CN >

As we learned earlier, Pandas in Python is an open source module that we can use for data analysis and making machine learning models. It is Numpyused along with another package called as they go hand in hand to support multidimensional arrays.

Many data science modules can be used with Pandas in the Python ecosystem. We will learn different operations to append to an empty DataFrame in Pandas.

A DataFrame is two-dimensional and potentially heterogeneous data in a tabular form.

This tutorial teaches how to add rows and columns to an empty DataFrame in Pandas using Python. We will look at three different ways to add data to an empty DataFrame, as there are multiple ways.

Creating an Empty DataFrame and Adding Rows and Columns in Pandas

Below is the code to import the required packages, make an empty DataFrame, and append the columns.

# import pandas library as pd
import pandas as pd

# create an Empty DataFrame object
df = pd.DataFrame()
print(df)
# append columns to an empty DataFrame
df["Name"] = ["Preet", "Parin", "Rajesh"]
df["Articles"] = [97, 600, 200]
df["Improved"] = [2200, 75, 100]

print(df)

The output of the above code snippet is as follows.

Empty DataFrame
Columns: []
Index: []
	Name	Articles  Improved
0	Preet	97		  2200
1	Parin	600		  75
2	Rajesh 	200		  100

Before we add columns and rows, the DataFrame is empty. Hence, printing the empty DataFrame gives us the output Empty DataFrame, Columns: [], Index: []as the output, which is expected since the data is empty.

Append rows to an empty DataFrame with Pandas columns

In this method, the DataFrame will be empty but will have predefined column names and our only task is to insert data in the rows below it.

Below is the code for the above method, we initially import the library Pandas, create a DataFrame with columns and then append the values ​​in the form of rows.

# import pandas library as pd
import pandas as pd

# create an Empty DataFrame
# object With column names only
df = pd.DataFrame(columns=["Name", "Articles", "Improved"])
print(df)

# append rows to an empty DataFrame
df = df.append({"Name": "Preet", "Articles": 97, "Improved": 2200}, ignore_index=True)
df = df.append({"Name": "Parin", "Articles": 30, "Improved": 50}, ignore_index=True)
df = df.append({"Name": "Rajesh", "Articles": 17, "Improved": 220}, ignore_index=True)
print(df)

This code will give us the following output.

Empty DataFrame
Columns: [Name, Articles, Improved]
Index: []
	Name	Articles  Improved
0	Preet	97		  2200
1	Parin	30	      50
2	Rajesh	17		  220

As we can see, since we have added the names of the columns in the DataFrame, the output Columns: [Name, Articles, Improvement]consists of which are the column names in an array.

The following output is because we used .append()the function.

Use the Pandas .loc()function to create an empty DataFrame, including a column with an index and additional rows

The retrieve method in Pandas .loc()helps the user to retrieve values ​​from DataFrame easily without any complexity. The values ​​in a particular row and column can be accessed based on the index value passed in the function.

In this method, we will create an empty DataFrame and the column names. Each column will be identified using the index to access it.

Next we will append the rows one by one.

The code demonstrating this approach is given below.

# import pandas library as pd
import pandas as pd

# create an Empty DataFrame object With
# column names and indices
df = pd.DataFrame(columns=["Name", "Articles", "Improved"], index=["a", "b", "c"])

print("Empty DataFrame With NaN values : \n\n", df)

# adding rows to an empty
# dataframe at existing index
df.loc["a"] = ["Preet", 50, 100]
df.loc["b"] = ["Parin", 60, 120]
df.loc["c"] = ["Rajesh", 30, 60]
print(df)

The output of the code given above is as follows.

Empty DataFrame With NaN values :

   Name  Articles  Improved
a  NaN      NaN      NaN
b  NaN      NaN      NaN
c  NaN      NaN      NaN
   Name  Articles  Improved
a  Preet	50		 100
b  Parin	60	     120
c  Rajesh	30		 60

As we can observe, NaNthe values ​​in the first table are there because we mentioned the index, but the values ​​are still empty.

Providing the number of indexes will bind the table to that many values ​​for that particular instance. Increasing the number of indexes allows more values ​​to be inserted.

So, in this tutorial, we learned three different ways to append values ​​to a DataFrame in Pandas.