Around 80 percent of data scientists spend their time cleaning datasets using functions. More so, most of these functions come from Pandas.

Pandas is a Python library whose function is to manipulate and analyze data.

In a Kaggle survey, when a question was asked which would be the most preferred programming language they would recommend any aspiring data scientist to learn, nearly 79 percent said Python. Python programming language has always been a data science specialist’s preferred programming language.

Easy to read and learn, this dynamic programming language offers some of the most powerful libraries for those looking to build a data science career.

Having said that, let us talk about the top five Pandas functions crucial for all data scientists:

  • merge( )

As the name mentions, this function allows data frames to merge together depending on the key column and index. Some of the input parameters are:

How- you need to choose the type of join you need. For this, you’ll have to choose from {right, left, inner, cross, and outer}. The default one is an inner join.

As a data scientist, you need to understand merge since you’re be dealing with multiple datasets. You might also face a situation wherein 1 dataset will have a particular column you need and another dataset will have another column. Therefore, instead of going back and trying to find which column to use, you can simply merge both the columns.

  • groupby( )

.groupby( ) is an advanced Pandas function that creates different groups according to the values given in the column. The major input parameter is “by” through which it further equals a column you’re looking to group. For instance, say you have a data frame of 1000 people along with details of their country they reside in and their income. Now if you just want to see just their salaries, you can use the function .groupby( ) using “by” equals the country they’re living in. Along with this function, an aggregation function is also used i.e. mean, min, max, and sum.

Here’s how the code would most likely look:

 df.groupby(by = [‘Country’]).mean()

  • loc[row, column]

This is another significant function in the Pandas library and is used for indexing data frames to gain access to certain rows and columns. The input parameters will include the rows you’re looking to index in the first input, and the columns can be your second. Now, these inputs could simply be a label or an integer.

As a data science specialist, it is essential that you learn all of these functions for better data analysis.

  • drop_duplicates( )

Not all data you receive are always perfect. Most of the datasets might have duplicate values, incorrect types in a column, or perhaps even have missing values. Using .drop_duplicates( ), you don’t need to work about choosing the wrong data anymore. This function takes care of such problems however, it lets you decide.

The input parameters are:

  • Keep – this helps identify which duplicate to keep ‘first’: The drop duplicate except for the first occurrence i.e. ‘last’: And it duplicates except for the last occurrence i.e. ‘false’: drop all of the duplicates.
  • Subset – instead of dropping the duplicates from the entire data frame, it helps remove duplicates from the columns of your input.
  • apply( )

As the name suggests, .apply() helps apply functions to any row or column. This input could be a particular function and this function either already exists in Python or you could create your own.

However, before you move forward to use any of the functions mentioned above, you need to first import all your datasets into Python. Since most of these datasets are in CSV format, you would need to import these datasets in Python using the .read_csv() function.