Python CSV Handling

A Python module for reading and writing CSV files.

A file object is returned when you open a file using the open() function in Python. It allows you to interact with the file, reading or writing data.

The csv.reader object from the Python csv module reads the contents of a CSV file as a sequence of rows, each row being a list of strings representing the values of the respective fields.

The first row of a CSV file often contains column headers. You can use the next() method with the csv.reader object to obtain this header row, which is then typically stored in a list for later use.

The with statement automatically closes the file after it is used, preventing resource leaks. This is a safer and more efficient way to manage open files compared to manually closing them with close().

The csv.writer object helps in writing data to a CSV file. It provides methods like writerow to write a single row and writerows to write multiple rows at once.

The writerow method of the csv.writer object writes a single row of data to the CSV file.

The writerows method of the csv.writer object efficiently writes multiple rows of data to the CSV file at once.

The open function takes the filename and a mode as input (like 'w' for writing) and returns a file object, which represents the opened file.

The with statement ensures that the file is automatically closed after the code block completes, preventing resource leaks. This is a safer and more efficient approach than explicitly closing the file.

A method in Pandas used to display the first few rows of a DataFrame, by default showing the first 5 rows.

A method in Pandas used to display the last few rows of a DataFrame. You can specify the number of rows to display using the 'n' argument.

A method in Pandas used to generate summary statistics of numeric columns in a DataFrame, including count, mean, standard deviation, range, and quartiles.

A method in Pandas that can be used with df.describe() to customize the percentiles used in the summary statistics. For example, you could display the 30%, 50%, and 70% percentiles.

A method in Pandas that can be used with df.describe() to filter the summary statistics to only include specific data types. For example, you could get summary statistics for only numeric columns by using 'include='number'.