5 Most Important Pandas Functions For Data Analyst

read_csv() This is one of the most crucial pandas methods in Python. read_csv() function helps read a comma-separated values (csv) file into a Pandas DataFrame. All you need to do is mention the path of the file you want it to read. It can also read files separated by delimiters other than comma, like | or tab. head() head(n) is used to return the first n rows of a dataset. By default, df.head() will return the first 5 rows of the DataFrame. If you want more/less number of rows, you can specify n as an integer. describe() describe() is used to generate descriptive statistics of the data in a Pandas DataFrame or Series. It summarizes central tendency and dispersion of the dataset. describe() helps in getting a quick overview of the dataset. drop_duplicates() drop_duplicates() returns a Pandas DataFrame with duplicate rows removed. Even among duplicates, there is an option to keep the first occurrence (record) of the duplicate or the last. You can also specify the inplace and ignore_index attribute.

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