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Python: Pandas, Working with DataFrames


Sure DataFrames look nice, but how can I work with them?

Let’s cover some basic tasks in pandas to get you started.

Let’s start by building a DataFrame

I don’t like where they placed Age on my dataframe. I want to move it.

To do so, we are going to cover a couple of new terms: axis, drop() and insert()


Using numpy and pandas, you will come across many functions that require you to enter an axis as a parameter. Axis 0 is your rows while Axis 1 is your columns. This is due to the way matrices are named with a 3×2 matrix having 3 rows and 2 columns and a 2×3 having 2 rows and 3 columns


To move the age column, I am first going to create a copy of my dataframe minus the age column. To do this, I am going to use the drop() function. The drop() function accepts two arguments drop(name, axis). In our case name = ‘Age’ and axis = 1 since we are referring to a column.


Now we want to insert the age column. The syntax for the insert() function is insert(insert point, name, data)

add a new column

Adding a new column is straight forward. Just DataFrame[new column name] = value.

Below I created at new column called ‘Age When Start’ that shows the age of employees when they started. I derived this value by subtracting Years Service column from Age column.

boolean column

You can create a boolean column using a boolean operator.


You can sort a dataframe by any column using sort_values()

Sort is set to ascending by default. To reverse it, set ascending = False

** remember in Python, True and False need to start with a capital letter.


by rows

slicing by rows is just like with a list

by columns

slicing by columns is a bit more complex. To slice by column name you have to use the dataframe.ix command.

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