How to use Python for data cleaning?

In the field of data analysis, data cleaning is a very important link. Data cleaning includes identifying and correcting any errors in the data, characterizing and processing missing or invalid information, etc. In Python, there are many libraries that can help us with data cleaning. Next, we will introduce how to use Python for data cleaning.

1. Loading data

In Python, you can use the pandas library to load data. Of course, the type of data needs to be checked before data cleaning. For CSV files, the read_csv() function in pandas can help us easily load data:

import pandas as pd

data = pd.read_csv('data.csv')

If the data is an Excel file, use the read_excel() function. If the data comes from a relational database, use SQLAlchemy or another database package to obtain the data.

2. Identify data errors

The first step in data cleaning is to identify data errors. Data errors include:

1. Missing Values

It is very common to have missing values ​​in your data. We can use the isnull() or notnull() function of the pandas library to detect whether there are missing values ​​in the data:

data.isnull()
data.notnull()

2. Outliers

Outliers are irregular data that do not match other data points in the data set. Outliers can be detected using statistical methods, such as dividing the data into quartiles, deleting data points larger than a certain standard deviation value, etc. Of course, you can also use visualization methods such as box plots and scatter plots to detect outliers.

3. Duplicate data

Duplicate data means that multiple records in the data display the same data value. You can use the pandas library's duplicated() and drop_duplicates() functions to detect and remove duplicate data.

data.duplicated()
data.drop_duplicates()

3. Data Cleaning

After identifying data errors, the next step is data cleaning. Data cleaning includes the following steps:

1. Filling in null values

When there are missing values ​​in the data, one method is to delete these records directly. However, deleting records may affect the integrity of your data. Therefore, we can use the fillna() function to replace null values ​​with the mean, median, or other special values:

data.fillna(value=10,inplace=True)

2. Delete null values

We can use the dropna() function to delete null values ​​in the data:

data.dropna()

3. Replace exception Value

If the created outliers will lead to inaccurate analysis of the data set, we can consider deleting these outliers; if deletion will affect the usefulness of the data, we can consider removing the outliers Replace with a more accurate estimate:

data.quantile(0.95)
data[(data < data.quantile(0.95)).all(axis=1)]

4. Save the cleaned data

After completing the data cleaning, we need to save the data. Data can be saved to a CSV or Excel file using the to_csv() and to_excel() functions of the pandas library:

data.to_csv('cleaned_data.csv')
data.to_excel('cleaned_data.xlsx ')

5. Conclusion

In the field of data analysis, data cleaning is a very important link. We can use Python and pandas libraries for data cleaning. Data cleaning includes identification and cleaning of data errors, identification of null values ​​and outliers, and data cleaning. Once the data cleaning is completed, we can save the data to a file for further analysis and visualization.

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