Aishwarya Nevrekar Profile picture
May 3 8 tweets 4 min read Twitter logo Read on Twitter
Master data cleaning in pandas like a pro with these simple functions.

Data cleaning is a critical step in any data analysis project

Learn to clean messy datasets with these easy steps.

A thread🧵👇 Image
In pandas, you can use functions like .dropna(), .fillna(), and .replace() to clean up your data.

For example, let's say you have a data frame with missing values like this: Image
1. The .dropna() function removes any rows with missing values.

For example, you can use df.dropna() to drop any rows with missing values from your data frame called "df": ImageImage
2. The .fillna() function replaces missing values with a specified value.

For example, you can use df.fillna(0) to replace any missing values in your data frame called "df" with 0: ImageImage
3. The .replace() function replaces specified values with another value.

For example, you can use df.replace('Yes', 'True') to replace all instances of 'Yes' in your data frame called "df" with 'True': ImageImage
4. Use functions like .str.lower() and .str.upper() to convert string values to lowercase or uppercase, respectively.

For example, you can use df['City'].str.lower() to convert all values in the 'City' column of your data frame to lowercase: ImageImage
With these simple functions, you can quickly and easily clean up your data in pandas and prepare it for analysis! #pandas #datacleaning #datascience
Thank you so much for reading this, for more follow @nevrekaraishwa2

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