Oneline Python Codes: 10 Python One-Liners That Will Boost Your Data Preparation Workflow

Oneline Python is an important stage in data based projects. Data preparation is a crucial step within the project development cycle where we prepare the raw data through a series of subsequent processes. Python plays a very major role in the data preparation life cycle.

We will learn about some of the Python online codes in this blog, which will help us in our data preparation journey. Python language is popular for short coding structure. These one liners are special methods created to perform crucial specific tasks during data processing and preparation.

What is Data Preparation?

Data Preparation is a method which is also known as data processing or data cleaning. It is a process of transforming a raw data into a suitable, clean and optimised format suitable for analysis and modeling purposes.

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The data preparation process consists of a series of stage which include data collection, data cleaning, data transformation, data validation, data formatting, data splitting and more. It helps in improving the quality of the data collected and making it suitable for analytic purposes. It also helps to make data suitable for training and decision making purposes.

Why Choose Python One Liners For Data Preparation?

Python is a high level language suitable for advanced artificial intelligence and machine learning models. Python can easily integrate with advanced level mechanisms and help us produce desired results within seconds. We have many advanced Python libraries that help us perform crucial tasks in just one line easily.

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Python due to its extensive community, libraries and easier code format are prioritised for data preparation as compared to other programming languages.

10 Python One Liners For Data Preparation Work

Let us now check some of the frequently used Python one liners for Programmers nowadays.

1. Multiple Regression with Pivot Data

The process of pivoting data into a more suitable analysis format is important. Let us check online python code to perform multiple regression with pivoted data.

import pandas as pd; from sklearn.linear_model import LinearRegression

df = pd.read_csv(“data.csv”).pivot(index=”Month”, columns=”Region”, values=”Sales”).fillna(0); X, y = df.drop(“Target_Column”, axis=1), df[“Target_Column”]

LinearRegression().fit(X, y)

This code snippet is used to create a pivot table which is used to summarise the data included based on months, sales and target column.

2. Apply a Function to a Column

This oneline python code can be used to apply a specific function using Python. You can easily apply with the help of the “lambda” function. You have to use .apply() to transform the existing column and you can double each value and store it in a new column.

df[“new_column”] = df[“existing_column”].apply(lambda x: x * 2)

3. Save DataFrame to a CSV File

You can easily use oneline python code to save a dataframe file to a CSV format. In this you have to use a single output.csv to export the file in CSV format. If you will use index = false it will prevent saving the index column.

df.to_csv(“output.csv”, index=False)

df.to_excel(“output.xlsx”, index=False)

4. Filter Rows Based on Condition

You have to filter the rows based on a condition you can use Python Panda library to execute it using oneline python code. Suppose you have to sort a row where you have to select a number greater than 100.

df_filtered = df[df[“column_name”] > 100]

This oneline Python code will help you filter the column where the value is greater than 100.

5. Fill Missing Values with Mean

You have to fill missing places with some missing number. Suppose you want to fill the missing values with the mean value. It will replace the NaN values with the column mean.

df.fillna(df.mean( ), inplace=True)

The implace value which is set to true modifies the dataframe directly.

6. Remove Missing Values

You can use this oneline python code to delete or eliminate any missing value in the dataframe. You only need to use .dropna( ) to initiate the effect and all the NaN values will be deleted.

df = df.dropna()

7. Read a CSV File into DataFrame

You can use this oneline python code to read a CSV file simply. This function is available in the Python Pandas library. You only have to use .read_csv( ) to initiate the affect as it loads the CSV file into a pandas dataframe.

import pandas as pd

df = pd.read_csv(“data.csv”)

8. Transformation using Pipe( )

Data preparation uses this chain transformation to filter filter rows, columns and sort data in preferred order. It can make any chain of code simpler and easier to understand. This will eliminate the need to use intermediate variables and process custom functions in an predefined order.

df = df.pipe( )

9. Combine Multiple Columns into One

You can combine multiple columns into one cell using a simple .sum( ) function. You can choose either sum row wise or column wise based on your preference.

df[“total”] = df[[“col1”, “col2”, “col3”]].sum(axis=1)

10. DataFrame Query Optimisation with eval( )

You can easily customise your dataframe query using the .eval() function. This oneline python code will help you create a new column by subtracting using simple row wise operation. You can also combine multiple operation in one time using the .eval() function.

df = df.eval(“profit = revenue – cost”)

Why are Python OneLiners Efficient For Big Projects?

Python one liners are very useful in solving a complex problem faster and it also makes your code reusable for a specific set of problems hence you do not need to write the same code for the same type of problem again and again.

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As compared to other languages python codes are readable and more compact. Python provides an extensive set of libraries with methods consisting of specific functions. In complex projects these oneline python codes will help you save much time of developers as well as make the resource utilisation more efficient and powerful.

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Oneline Python Codes FAQs

Q1. Is Python language used for data preparation?

Ans: Yes Python being a simple and high level language consisting of a huge collection of libraries are often used for oneliner concise code which help developers write short and simple code.