Selecting an Individual Cell

Let's take a closer look at the 'DATE' column in our DataFrame. We can use the double square bracket notation to look at the second entry in the column: 

df['DATE'][1]

Alternatively, for column names no spaces, we can also use the dot-notation:

df.DATE[1]

I prefer the square bracket notation for column names since it's more flexible, but with the dot notation, you get to use autocomplete, which is also nice.

Inspecting the Data Type

When we type check the contents of this cell, we see that we are not dealing with a date object, but rather with a string.

This is not very handy. Not only will the string format always show the unnecessary 00:00:00, but we also don't get the benefit of working with Datetime objects, which know how to handle dates and times. Pandas can help us convert the string to a timestamp using the to_datetime() method.

Here's how we can convert the entry in our cell and check that it worked:

Let's use Pandas' to_datetime() to convert the entire df['DATE'] column.

Excellent. Now we can start thinking about how to manipulate our data so that we get a one column per programming language. For all of that and more, I'll see you in the next lesson.