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Advanced Data Wrangling and Cleaning Techniques

Advanced Data Wrangling and Cleaning Techniques for Data Science and Analysis with Python

Data wrangling and cleaning are essential steps for data science and analysis with Python. This guide will help you understand the advanced techniques to wrangle and clean data in Python.

Identifying and Removing Outliers

Outliers are data points that are significantly different from the rest of the dataset and can interfere with analysis. To identify and remove outliers, you can use boxplots and the zscore function. A boxplot is a graphical representation of the data that shows the median, the upper and lower quartiles, the minimum, the maximum, and the outliers. With the zscore function, you can calculate the z-score for each data point, which is the number of standard deviations that the data point is away from the mean.

After identifying the outliers, you can remove them from the dataset using the pandas drop function. For example:

df.drop(df[df.zscore > 3].index)

Data Transformation

Data transformation is the process of transforming data into a different format. This can be done using the map and apply functions in pandas. The map function takes a dictionary as an argument and maps the keys to the values. The apply function takes a function as an argument and applies the function to each row in the dataframe. For example:

df['column_name'] = df['column_name'].map({'a': 'b', 'c': 'd'})
df['column_name'] = df['column_name'].apply(lambda x: x**2)

Data Imputation

Data imputation is the process of filling in missing values in a dataset. This can be done using the fillna function in pandas. This function takes a dictionary as an argument, with the keys being the column names and the values being the values to be imputed. For example:

df.fillna({'column_name': 0})

Tips for Data Wrangling and Cleaning with Python

  • Use the describe function: The describe function in pandas can be used to get a summary of the data, which can be useful for identifying outliers.
  • Check for missing values: Use the isnull function to check for missing values in the dataset.
  • Check for duplicates: Use the duplicated function to check for duplicates in the dataset.

Data wrangling and cleaning are important steps for data science and analysis with Python. With the advanced techniques discussed in this guide, you can confidently wrangle and clean data in Python.