How can I filter multiple dataframes with multiple conditions (pandas)?

Question

I have this dataframe:

Company	Version	Disp Version	complement	Value
1	1	0	1	100
1	1	0	2	200
1	2	1	1	300
1	2	1	2	400
2	1	1	1	500
2	1	1	2	600
2	2	1	1	700
2	2	1	2	800
3	1	1	1	900
3	1	1	2	1000
4	1	0	1	1100
4	1	0	2	1200
4	2	0	1	1300
4	2	0	2	1400
4	3	0	1	1500
4	3	0	2	1600
5	1	0	1	1700
5	1	0	2	1800
5	2	0	1	1900
5	2	0	2	2000
5	3	0	1	2100
5	3	0	2	2200
5	4	1	1	2300
5	4	1	2	2400
6	1	0	1	2500
6	1	0	2	2600
6	2	0	1	2700
6	2	0	2	2800
7	1	1	1	400
7	1	1	2	400

I want my dataframe to be filtered with some conditions:

1. If the column 'Company' value has 'Disp Version' equals to 1 or 0, you need to get the rows that have 'Disp Version' equals to 1 and the max of the column 'Version';
2. If the column 'Company' value has 'Disp Version' equals only to 1, you need to get max of the column 'Version';
3. If the column 'Company' value has 'Disp Version' equals only to 0, you need to get max of the column 'Version'.

Furthermore, you need to have for each 'Company' value, the values 1 and 2 for the column 'complement'.

Examples:

For the first condition I need a dataframe like this:

Company	Version	Disp Version	complement	Value
1	2	1	1	300
1	2	1	2	400
5	4	1	1	2300
5	4	1	2	2400

For the second condition I need a dataframe like this:

Company	Version	Disp Version	complement	Value
2	2	1	1	700
2	2	1	2	800
3	1	1	1	900
3	1	1	2	1000
7	1	1	1	400
7	1	1	2	400

For the third condition I need a dataframe like this:

Company	Version	Disp Version	complement	Value
4	3	0	1	1500
4	3	0	2	1600
6	2	0	1	2700
6	2	0	2	2800

I need this output (with is the 3 dataframes together):

Company	Version	Disp Version	complement	Value
1	2	1	1	300
1	2	1	2	400
2	2	1	1	700
2	2	1	2	800
3	1	1	1	900
3	1	1	2	1000
4	3	0	1	1500
4	3	0	2	1600
5	4	1	1	2300
5	4	1	2	2400
6	2	0	1	2700
6	2	0	2	2800
7	1	1	1	400
7	1	1	2	400

Answer 1

    1. Get maximum version number for each company
    maxv = df.groupby('Company')['Version'].max()

    2. Join with the original dataframe
    merged_df = pd.merge(df, maxv, on=['Company'])

    3. Get companies which satisfy the conditions (have both 1 and 0)
    idx = df.groupby('Company').apply(lambda g: g['Disp Version'].sum() < g['Disp Version'].count() and g['Disp Version'].sum() > 0)
    valids = idx.loc[idx ==True]

    4. Final result
    df.loc[df['Company'].isin(valids.index) & df['Disp Version'] == 1]