Creating a new dataframe where a field is blank in the original dataframe

Using Python3 and Pandas. I am admittedly pretty new and I'm having a hard time searching for an answer to this question.

I have a dataframe that contains lots of information and I'm trying to get a dataframe that is just the items where one specific field in the original is blank.

I have queried my database to get a dataframe I am calling full_df which is all information on all items in the database. I want to now create a new dataframe that selects just the items where one field in full_df is blank.

This is what I've tried:

no_rate = full_df[(full_df['rate'] == "")]

Which is returning nothing even though I know for a fact that there are loads of items where 'rate' is blank. I expected the dataframe no_rate to be populated with all the items where 'rate' is blank.

How do I select those items for this new dataframe?

There are a few things you need to do. First of all, is the data type of your rate column a string, or object? df.dtypes will tell you. If not, then you can't test it against "".

Second, and more to the point, a way to do a conditional select is by useing loc.

So, if your rate column looks like this

df = pd.DataFrame({'Rate': ['good', 'good', 'bad', 'medium', '', 'bad', '', 'good']})
df

	Rate
0	good
1	good
2	bad
3	medium
4	
5	bad
6	
7	good

then you could write

df.loc[df['Rate']==""]

and get

	Rate
4	
6	

which is actually showing you the contents, but since there is nothing in there, it looks like just the row numbers. Let's add another column to see the results more plainly.

df['Color'] = ['Red', 'Blue', 'Yellow', 'Red', 'Yellow', 'Red', 'Green', 'Blue']
df
	Rate	Color
0	good	Red
1	good	Blue
2	bad	Yellow
3	medium	Red
4		Yellow
5	bad	Red
6		Green
7	good	Blue

and

df.loc[df['Rate'] == ""]

shows

	Rate	Color
4		Yellow
6		Green

So, what if your rate is actually a number

df['Decimal_Rate'] = [.8, .8, .3, .6, np.nan, .2, np.nan, .9]
df
	Rate	Color	Decimal_Rate
0	good	Red	0.8
1	good	Blue	0.8
2	bad	Yellow	0.3
3	medium	Red	0.6
4		Yellow	
5	bad	Red	0.2
6		Green	
7	good	Blue	0.9

if you wanted to isolate the empty cells of numbers, you can go like this:

df.loc[df['Decimal_Rate'].isna()]

which results in

	Rate	Color	Decimal_Rate
4		Yellow	
6		Green