Dropping rows based on index and condition

Question

I want to drop certain rows based off of range of rows: I feel like it would look something like the below

df.drop(df.index[[0,1,2,3]])

so that will just drop my first two rows but I would like to drop those rows if and only if that range of rows is blank. so

  Name

  Macy
  June

Jackson

So I would only want to go through the dataframe and drop rows in that range if and only if their is no data so my new dataframe would look like this:

  Name
  Macy
  June
Jackson

Answer 1

You don't need the df.index in the drop statement. However, if you only want to keep the rows where the Name column is filled, you just can use:

> df_filter = df [df.Name != '']

If you really want to locate the rows based on index, you can use

> df_filter = df.drop([0,2])

Answer 2

You can use df.ix to get the excepted items, then drop the None s using dropna() method:

df.ix[indices].dropna()

Demo:

In [48]: df
Out[48]: 
      name
0     None
1     Macy
2     June
3     None
4  Jackson

In [49]: df.ix[[0,1,2,3]].dropna()
Out[49]: 
   name
1  Macy
2  June

Note: As mentioned in comment, since the ix method is deprecated you can use df.loc() instead.