How can I count values for each date in a Dataframe based conditionally on the value of a column?-CodePudding

I have a dataframe with xenophobic and non-xenophobic tweets. For each day, I want to count the number of tweets that have a sentiment of 1.

This is the Dataframes df_unevaluated

  sentiment id                  date                  text
0   0   9.820000e 17    2018-04-05 11:43:31 00:00   but if she had stated another fact like that I may have thought...
1   0   1.170000e 18    2019-09-03 22:53:30 00:00   the worst thing that dude has done this week is ramble about the...
2   0   1.140000e 18    2019-06-28 17:43:07 00:00   i think immigrants of all walks of life should be allowed into...
3   0   2.810000e 17    2012-12-18 00:43:57 00:00   why is america not treating the immigrants like normal people...
4   1   8.310000e 17    2017-02-14 01:42:26 00:00   who the hell wants to live in canada anyhow the people there...
...

This is what I've tried:

      # Put all tweets with sentiment = 1 into a Dataframes

for i in range(len(df_unevaluated)):
    if df_unevaluated['sentiment'][i] == 1:
        df_xenophobic = df_xenophobic.append(df_unevaluated.iloc[[i]])

      # Store a copy of df_xenophobic in df_counts
df_counts = df_xenophobic

      # Change df_counts to get counts for each date
df_counts = (pd.to_datetime(df_counts['date'])
       .dt.floor('d')
       .value_counts()
       .rename_axis('date')
       .reset_index(name='count'))

      # Sort data and drop index column
df_counts = df_counts.sort_values('date')
df_counts = df_counts.reset_index(drop=True)

      # Look at data
df_counts.head()

This was the output:

           date                count
0   2012-03-14 00:00:00 00:00   1
1   2012-03-19 00:00:00 00:00   1
2   2012-04-07 00:00:00 00:00   1
3   2012-04-10 00:00:00 00:00   1
4   2012-04-19 00:00:00 00:00   1
...

This is what I expected:

              date              count
0   2012-03-14 00:00:00 00:00   1
1   2012-03-15 00:00:00 00:00   0
2   2012-03-16 00:00:00 00:00   0
3   2012-03-17 00:00:00 00:00   0
4   2012-03-18 00:00:00 00:00   0
5   2012-03-19 00:00:00 00:00   1
6   2012-03-20 00:00:00 00:00   0
7   2012-03-21 00:00:00 00:00   0
...

These are some links I've read through: