Why I Get Key Error Even Though Column Present in Pandas

Pandas - Getting a Key Error when the Key Exists

Your Trades dataframe has a single column with all the intended column names mashed together into a single string. Check the code that parses your file.

Keyerror even though column exists

It's the axis=1 in your .sort_values() that's causing problems: there is no such index 'Top' (but there is a column 'Top').

In other words, change your second line to:

temp_df = temp_df.sort_values(by='Top')

Why I get Key error even though column present in pandas?

Instead of manually entering the column names, try the following, as a test:

for col in data.columns:
    data[col] = data[col].astype(str)

This should also work for the entire dataframe

data = data.astype('str')

Pandas get_group returns key error, even though key is in group?

It seems to me that you don't want to groupby, but simply filter your dataframe.

>>> df[df['team'] == 'Manchester City']

Use groupby when you want to apply some logic to each group (i.e. aggregate them, filter them, modify/transform in some way etc). If you just want to list the groups, then all you need is slicing and .loc.

You may also sort your dataframe by team to see it in a "grouped" fashion.

>>> df.sort_values(by='team')

Now, as to why you get a KeyError: your data is likely not clean. The group name must match excatly the values in your DataFrame. For example, if you have in your df the value " Manchester City", with an extra whitespace in the beginning, calling get_group('Manchester City') will yield an error. The same is true for \n, \t and other invisible characters.

Make sure you have clean team names first. Usually, doing

>>> df['team'] = df['team'].str.strip()

is a good start.

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