Pandas make it easy to delete rows of a dataframe. There are multiple way to delete rows or select rows from a dataframe. In this post, we will see how to use drop() function to drop rows in Pandas by index names or index location.. Pandas drop() function can also be used drop or delete […]
Pandas 101
How to Combine Year, Month, and Day Columns to single date in Pandas
In this post, we will see how to combine columns containing year, month, and day into a single column of datetime type. We can combine multiple columns into a single date column in multiple ways. First, we will see how can we combine year, month and day column into a column of type datetime, while […]
How to Convert a Column to Datetime type with Pandas
Pandas in Python has numerous functionalities to deal with time series data. One of the simplest tasks in data analysis is to convert date variable that is stored as string type or common object type in in Pandas dataframe to a datetime type variable. In this post we will see two ways to convert a […]
Introduction to Data Cleaning with Pyjanitor
Data cleaning is one of the most common and important tasks of any data analysis. In typical data analysis setting, we would might get our dataset from excel/csv/tsv file and perform a series of operations to make the data cleaner. For example, we would start with cleaning the names of variables to make it consistent, […]
How To Code a Character Variable into Integer in Pandas
Often while working with a Pandas dataframe containing variables of different datatypes, one might want to convert a specific character/string/Categorical variable into a numerical variable. One of the uses of such conversion is that it enables us to quickly perform correlative analysis. In this post, we will see multiple examples of converting character variable into […]