Streamline Your Data Science Workflow: Essential 20 Pandas Functions You Must Master
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Hey guys, hope you are doing great. Pandas is a powerful data manipulation library in Python that is widely used in data science. In this blog, we will explore 20 pandas functions that can cover 80% of your data science tasks, from loading data to data cleaning, data exploration, and data manipulation.
read_csv(): This function is used to read data from a CSV file into a pandas DataFrame.head(): This function is used to view the first n rows of a DataFrame.info(): This function is used to display information about a DataFrame, including the number of rows, columns, and data types.describe(): This function is used to generate descriptive statistics about a DataFrame, including the mean, standard deviation, and quartiles.value_counts(): This function is used to count the number of occurrences of each value in a column of a DataFrame.isnull(): This function is used to check for missing values in a DataFrame.dropna(): This function is used to drop rows or columns that contain missing values.fillna(): This function is used to fill in missing values in a DataFrame.groupby(): This function is used to group a DataFrame by one or more columns and perform an aggregation function.pivot_table(): This function is used to create a pivot table from a DataFrame.merge(): This function is used to join two DataFrames together based on a common column.sort_values(): This function is used to sort a DataFrame by one or more columns.apply(): This function is used to apply a function to a DataFrame or a column of a DataFrame.map(): This function is used to map values from one set to another.astype(): This function is used to convert the data type of a column in a DataFrame.drop(): This function is used to drop a column or row from a DataFrame.set_index(): This function is used to set a column as the index of a DataFrame.reset_index(): This function is used to reset the index of a DataFrame.loc[]: This function is used to select rows and columns from a DataFrame using labels.iloc[]: This function is used to select rows and columns from a DataFrame using integer indexes.
By mastering these 20 pandas functions, you can cover 80% of your data science tasks, from loading data to data cleaning, data exploration, and data manipulation. With these skills, you can manipulate and analyze data more efficiently and effectively, and create more accurate and powerful machine learning models.