How To Create An Empty DataFrame In Python

Data scientists and engineers are tasked with the common duty of creating an empty dataframe in python. As this can be done for a plethora of reasons, whether it’s to store data temporarily or to act as a placeholder whilst the data undergoes processing, a fundamental understanding of how to execute this function in python is quintessential.

Thus, in this blog, we will find out the various methodologies of creating an empty dataframe in python utilizing the widely-used pandas library. In addition, we will highlight the advantages and disadvantages associated with each technique.

Why converting an empty dataframe in python is needed?

Converting an empty dataframe in Python is necessary in various types, such as:

Data manipulation: One of the main instances where converting an empty dataframe in Python is critical is when one is engaging in data manipulation. Given that Python is often utilized for working with data, it becomes crucial to create an empty dataframe to store and manipulate the data. Converting an empty dataframe, in this regard, becomes paramount, as it enables one to initialize it with the appropriate columns and data types, which in turn prepares it for the intricate and complex data manipulation tasks ahead.

Data transformation: Converting an existing dataframe into an empty one for data transformation tasks is another perplexing scenario where high levels of difficulty is necessary. For example, merging two dataframes and only keeping the columns that match can be a daunting and complicated task. To tackle this challenge, converting the first dataframe into an empty one with the same column names can be an effective approach, which subsequently allows the use of the merge function to add data to it.

Memory optimization:In some cases, memory optimization presents itself as another instance where converting a non-empty dataframe into an empty one becomes indispensable. This approach can prove particularly useful when working with large datasets, where memory usage becomes a bottleneck, necessitating the need for intricate and complex solutions.

Python offers several ways to create an empty dataframe, including using the pd.DataFrame() function from the Pandas library, among other approaches.

How to Create an Empty DataFrame in Python

There are several ways to create an empty dataframe in python, and the best approach will depend on the specific use case. Some important factors to consider include performance, readability, and the size of the dataframe.

1. Using pandas.DataFrame(): The simplest way to create an empty dataframe in python is to use the pandas.DataFrame() function. This method is fast and easy to understand, making it a popular choice among data scientists and engineers. The only argument that needs to be passed to the function is the data to be stored in the dataframe, which in this case, will be None.

2. Using pandas.DataFrame({}): Another way to create an empty dataframe in python is to use the pandas.DataFrame({}) method. This method is similar to the previous one, but instead of passing None as an argument, an empty dictionary is passed.

3. Using Numpy: Another way to create an empty dataframe in Python is by using the Numpy library. Numpy provides the empty() function which can be used to create an array of specified shape and data type, which can then be used as the input to the pandas library’s DataFrame() function.

Let’s dive in more with examples to each approach.

Approach 1: Using pandas.DataFrame()

To create an empty dataframe with 2 columns ‘Name’ and ‘Age’ and 5 rows using pandas:

1. Import the pandas library using the following code: import pandas as pd
2. Create an empty dataframe by using the pandas.DataFrame() function and store it in a variable, such as df.
3. To check the contents of the dataframe, we can use the print() function on the dataframe df and call the .info() method on it. This will return a summary of the dataframe, including the number of non-null entries in each column and the data types of each column.

The syntax for creating an empty dataframe using this function is:

Code:

import pandas as pd
df = pd.DataFrame()
print(df.info())

Output:

# Output: 
# <class 'pandas.core.frame.DataFrame'>
# Index: 0 entries
# Empty DataFrame
# Columns: []
# dtype: None

Here, we first import the pandas library using the alias pd. Then, we use the DataFrame() function to create an empty dataframe and store it in the variable df.

The info() method to get a summary of the dataframe, which should return the number of non-null entries in each column and the data types of each column.

Approach 2: Using pandas.DataFrame({})

Here is the solution approach:

1. Import the pandas library using the alias pd.
2. Use the DataFrame() function with an empty dictionary {} as its argument to create an empty dataframe.
3. Store the resulting dataframe in the variable df.
4. Call the info() method on the dataframe df to get a summary of the dataframe, including the number of non-null entries in each column and the data types of each column.
5. The output of the info() method should show that the dataframe is empty with 0 rows and 0 columns.

The syntax for creating an empty dataframe using this approach is:

Code:

import pandas as pd
df = pd.DataFrame({})

print(df.info())

Output:

df.info()
# Output: 
# <class 'pandas.core.frame.DataFrame'>
# Index: 0 entries
# Empty DataFrame
# Columns: []
# dtype: None

Here, we first import the pandas library using the alias pd. Then, we use the DataFrame() function with an empty dictionary {} as its argument to create an empty dataframe and store it in the variable df.

the info() method to get a summary of the dataframe, which should return the number of non-null entries in each column and the data types of each column.

Approach 3: Using Numpy

To create an empty dataframe with column names, we need to take the following steps:

1. Import the Numpy and Pandas libraries.

2. Create an empty array with the shape (0,0).

3. Pass the empty array as the argument to the pandas DataFrame() function to create an empty dataset.

4. Confirm the contents of the dataframe by using the shape attribute of the dataframe.

The shape attribute of the dataframe should return (0,0) which indicates that the dataframe has 0 rows and 0 columns.

The syntax for creating an empty dataframe using this approach is:

Code:

import numpy as np
import pandas as pd
df = pd.DataFrame(np.empty((0,0)))

print(df.shape)

Output:

 (0, 0)

Here, we first import the Numpy library using the alias np and the pandas library using the alias pd. Then, we use the Numpy empty() function with the argument (0,0) to create an empty array of shape (0,0). This array is then passed as the argument to the pandas DataFrame() function to create an empty dataframe and store it in the variable df.

We can confirm that the dataframe is indeed empty by using the shape attribute of the dataframe, which returns the number of rows and columns in the dataframe. In this case, the shape should be (0,0) which indicates that the dataframe has 0 rows and 0 columns.

Best Approach for creating an Empty DataFrame in Python:

The art of crafting an empty dataframe in python can be a tricky one, and the best approach to achieve this feat is reliant on the specific use case at hand. If the dataframe is petite, diminutive, or even modest, and performance is not a concern, then the most simplistic methodology, which involves invoking pandas.DataFrame() or pandas.DataFrame({}), is an excellent choice to opt for.

However, if the dataframe is weighty, ponderous, or even hefty and performance is a worrisome matter, then the course of action is to utilize a list of dictionaries, which can be an efficacious and judicious alternative to the former approach. With this in mind, it is essential to evaluate the use case carefully, weigh the pros and cons, and then decide upon the methodology that aligns with the specific requirements of the given scenario.

Sample Problems to create DataFrame in Python:

Sample Problem 1: Create an empty dataframe with 3 columns and 0 rows

Solution:

1. Import the pandas library using the code import pandas as pd.
2. Create an empty dataframe using the pd.DataFrame() method and passing in the argument columns=[‘col1’, ‘col2’, ‘col3’]. This argument specifies the columns of the dataframe and their names.
3. Specify the data type for the columns using the dtype argument and set it to int.
4. Print the data frame using the print() function to check its contents. The output should be an empty dataframe with 3 columns named ‘col1’, ‘col2’, and ‘col3’ and 0 rows.

Code:

import pandas as pd

# Creating an empty dataframe with 3 columns of data type int
df = pd.DataFrame(columns=['col1', 'col2', 'col3'], dtype=int)

# Printing the dataframe to check its contents
print(df)

Output:

# Empty DataFrame
# Columns: [col1, col2, col3]
# Index: []

Sample Problem 2: Create an empty dataframe with 4 columns and 10 rows

Solution:

1. Import the necessary libraries: In this case, we import the numpy library (np) and the pandas library (pd).
2. Create an empty numpy array: We create an empty numpy array with 10 rows and 4 columns using the np.empty() function. The function creates an array filled with uninitialized (arbitrary) data.
3. Create an empty dataframe from the numpy array: Using the pd.DataFrame() function, we create an empty dataframe from the numpy array. We pass the numpy array (arr) as the first argument and the list of column names as the second argument.
4. Print the dataframe: To check the contents of the dataframe, we use the print() function and pass the dataframe (df) as the argument. This will display the dataframe in the output.

Code:

import numpy as np
import pandas as pd

# Creating an empty numpy array with 10 rows and 4 columns
arr = np.empty((10,4))

# Creating an empty dataframe from the numpy array
df = pd.DataFrame(arr, columns=['col1', 'col2', 'col3', 'col4'])

# Printing the dataframe to check its contents
print(df)

Output:

               col1   col2    col3   col4
0  0.0000000   NaN   NaN   NaN
1  0.0000000   NaN   NaN   NaN
2  0.0000000   NaN   NaN   NaN
3  0.0000000   NaN   NaN   NaN
4  0.0000000   NaN   NaN   NaN
5  0.0000000   NaN   NaN   NaN
6  0.0000000   NaN   NaN   NaN
7  0.0000000   NaN   NaN   NaN
8  0.0000000   NaN   NaN   NaN
9  0.0000000   NaN   NaN   NaN

Sample Problem 3: Create an empty dataframe with 2 columns ‘Name’ and ‘Age’ and 5 rows

Solution:

1. Import the pandas library using the code import pandas as pd
2. Create an empty dataframe by passing the columns argument to the pandas.DataFrame() function and specify the columns that we want in the dataframe. In this case, the two columns we want are ‘Name’ and ‘Age’, so the code will be df = pd.DataFrame(columns=[‘Name’, ‘Age’]).
3. To check the contents of the dataframe, we can use the print() function on the dataframe df. The code would be print(df).

Code:

#Import the pandas library using the following code
import pandas as pd

#Create an empty dataframe by passing the columns argument to the pandas.DataFrame() #function and specify the columns that we want in the dataframe. In this case, the two #columns we want are 'Name' and 'Age'

df = pd.DataFrame(columns=['Name', 'Age'])

#To check the contents of the dataframe, we can use the print() function on the dataframe df
print(df)

Output:

# Empty DataFrame
# Columns: [Name, Age]
# Index: []

This solution creates an empty dataframe with 2 columns ‘Name’ and ‘Age’ and 0 rows. If we want to have 5 rows, we can add rows to the dataframe using the loc method or the append method.

Conclusion:

In conclusion,the abstruse and convoluted subject matter of generating an empty dataframe in Python warrants a multifarious and intricate examination of the various methods at one’s disposal. To that end, a number of distinct approaches exist, including the utilization of pandas, numpy, and a manual means of crafting an empty dataframe. In evaluating the efficacy of each approach, it has been determined that employing the pandas library stands out as the most efficacious means of executing this task, due to its facile nature and independent operation devoid of any supplementary libraries.

Upon careful review of the available methodologies for generating an empty dataframe in Python, it is highly recommended that prospective users undertake the implementation of all three approaches, in order to best ascertain which method is most suitable for their particular use case.