The data analysis process that involves identifying and correcting errors, inconsistencies, and missing values in a dataset.
The data analysis process that involves identifying and correcting errors, inconsistencies, and missing values in a dataset.
Before starting the data cleaning process, you need to import the necessary libraries such as pandas and numpy to work with the data.
Before starting the data cleaning process, you need to import the necessary libraries such as pandas and numpy to work with the data.
Import necessary libraries
Import necessary libraries
Load the data:
Load the data:
Title 2
Load the dataset into your Python environment using pandas. This can be done using the read_csv function or any other suitable function based on the file format.
Load the dataset into your Python environment using pandas. This can be done using the read_csv function or any other suitable function based on the file format.
Explore the data
Explore the data
Take a look at the dataset to understand its structure, size, and data types. You can use functions such as head(), tail(), info(), describe() to explore the data.
Take a look at the dataset to understand its structure, size, and data types. You can use functions such as head(), tail(), info(), describe() to explore the data.
Identify missing values
Identify missing values
Check for missing values in the dataset using functions such as isnull(), notnull(), and sum().
Check for missing values in the dataset using functions such as isnull(), notnull(), and sum().
Deal with missing values:
Deal with missing values:
Once you have identified the missing values, you can either drop them using the dropna() function or fill them using the fillna() function.
Once you have identified the missing values, you can either drop them using the dropna() function or fill them using the fillna() function.
Identify duplicates
Identify duplicates
Check for duplicates in the dataset using the duplicated() function.
Check for duplicates in the dataset using the duplicated() function.
Deal with duplicates:
Deal with duplicates:
Once you have identified the duplicates, you can drop them using the drop_duplicates() function.
Once you have identified the duplicates, you can drop them using the drop_duplicates() function.
Outliers are data points that lie far away from the majority of the data points. You can use statistical methods such as z-score or IQR to identify and deal with outliers.
Outliers are data points that lie far away from the majority of the data points. You can use statistical methods such as z-score or IQR to identify and deal with outliers.
Identify and deal with outliers:
Identify and deal with outliers:
the data is an important step in the data cleaning process to make sure the data is in a consistent format.
the data is an important step in the data cleaning process to make sure the data is in a consistent format.
Standardize and normalize data:
Standardize and normalize data:
Once you have completed the data cleaning process, save the cleaned data into a new file format or the same file format for further analysis.
Once you have completed the data cleaning process, save the cleaned data into a new file format or the same file format for further analysis.
Save the cleaned data
Save the cleaned data
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