How to drop all values that are 0 in a single column pandas dataframe?

Question

1. I have a pandas DataFrame that was created from some raw data, there are hundreds of lines so I will just show the first 10 rows.

2.        text
   
    0       0
    1       0
    2       0
    3       0
    4  26.529
    5       0
    6  25.558
    7       0
    8       0
    9       0
   

3. I want to get rid of all the zeros in my data frame and replace the column name from 'text' to 'Results', so the final data should look like this:

4.       Results
   
    0    26.529
    1    25.558
   

5. My method was to use the df.drop() method to drop all rows containing zeros. My code looks like this:

    df = df.drop(df[df['text'] == 0].index,inplace=True)
   
    # I didn't write the code to replace to column name yet
   

6. Somehow when I run this, the resulting df is empty/ nonetype. I have no idea why the drop method just dropped everything in my dataframe. Please help! Much appreciated in advance!

7. When I debug the code in debug mode (vs code), I see the values in my df are as follows:

    I noticed that every element in my df is an object type. I want to get rid of all the arrays with an empty object. Ex. "000:array([''],dtype=object)"
   
   
    [1]: https://i.stack.imgur.com/yk63P.png
   

Answer 1

You can do that with the following

df[df["text"].str.strip()!="0"].rename(columns={'text':'Results'}).reset_index(drop=True)

Answer 2

I found a solution for this problem:

First I converted the data type from object to float64 in my df:

    df['text'] = pd.to_numeric(df['text'])

Then I proceeded to drop the 'nan' values from the df using:

    df = df.dropna()

This works for me!