5 Pandas Functions for Data Wrangling

Data wrangling is the process of cleaning and transforming raw data into a structured format which can be analyzed. Pandas is a very popular library for data manipulation, offers various functions to make the process of data wrangling easier and more efficient. In this blog, we will discuss five essential Pandas functions for data wrangling and provide code examples to see how we can use this in Python.

1 – dropna(): One common issue with raw data is missing values, which can cause errors in data analysis. The dropna() function removes any rows or columns that contain missing values.

2 – fillna(): we can use fillna() function to replace missing values with a specific value or method. The fillna() function can be used with constant or to fill them with a value based on a specific method.

3 – replace(): The replace() function is used to replace specific values in a DataFrame or Series with a new value.

4 – groupby(): The groupby() function is used to group rows of data based on a specified column or set of columns. This function is useful for data aggregation and summarization.

5 – pivot_table: pivot_table function allows you to transform a DataFrame by aggregating and reshaping the data. It is a way to summarize data into a tabular format that is more meaningful and easier to analyze.


In this article we discussed about 5 Pandas Functions which we can use for Data Wrangling. I hope you liked this article and if you have any question then let me know.

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