Find all rows that have 3 or more columns that satisfy a condition Pandas

Question

I have a dataframe that contains a players form and appearances as floats for 5 seasons. My goal is to remove all players who have played less than 2 seasons.

Here is an example of the dataframe

Here is a link to the dataframe in a csv file on Google Drive

https://drive.google.com/file/d/1gfkPJlfoyl2sE2j8VqJYyqHJGBMXMfkO/view?usp=sharing

An example of players I would like to remove is Aaron Hughes who only played two seasons 13/14 & 12/13 or Abdoulaye Diaby who only played in the 14/15 season.

An example of a player I want to keep is Abdoulaye Keita who has played at least 4 seasons.

My expected output would be :

Players club    16/17   15/16   14/15   13/14   12/13   17/18
    Form    Apps    Form    Apps    Form    Apps    Form    Apps    Form    Apps    Form    Apps
    0   Aaron Cresswell West Ham United 2.269231    26.0    3.108108    37.0    2.921053    38.0    0.000000    0.0 0.000000    0.0 2.400000    30.0
    1   Aaron Hunt  Hamburger SV    2.590909    22.0    2.136364    22.0    1.800000    15.0    3.741935    31.0    4.035714    28.0    2.500000    24.0
    2   Aaron Lennon    Everton 1.818182    11.0    2.760000    25.0    2.217391    23.0    2.555556    27.0    3.147059    34.0    1.913043    23.0
    3   Aaron Ramsey    Arsenal 2.173913    23.0    3.096774    31.0    3.241379    29.0    4.956522    23.0    1.833333    36.0    4.300000    20.0
    4   Abdoul Camara   En Avant de Guingamp    0.000000    0.0 2.235294    17.0    0.000000    0.0 1.500000    10.0    1.666667    15.0    0.000000    0.0
    5   Abdoulaye Doucoure  Watford 1.750000    20.0    4.558333    31.0    2.600000    35.0    3.750000    20.0    3.250000    4.0 3.000000    32.0
    6   Abdoulaye Keita Le Havre AC 1.571429    21.0    1.000000    4.0 2.000000    5.0 1.200000    5.0 0.000000    0.0 0.00

Any help would be greatly appreciated! Thanks

Answer 1

**Here is a better solution to your question.**

df['count'] = 0
df.loc[df['16/17']['Apps']>9.0, 'count'] += 1
df.loc[df['15/16']['Apps']>9.0, 'count'] += 1
df.loc[df['14/15']['Apps']>9.0, 'count'] += 1
df.loc[df['13/14']['Apps']>9.0, 'count'] += 1
df.loc[df['12/13']['Apps']>9.0, 'count'] += 1

df[df['count']>3]

Answer 2

x = 0
i = 0
array = []

while i < len(df):
    if df['16/17']['Apps'][i] > 9.0:
        x += 1
    elif df['15/16']['Apps'][i] > 9.0:
        x += 1
    elif df['14/15']['Apps'][i] > 9.0:
        x += 1
    elif df['13/14']['Apps'][i] > 9.0:
        x += 1
    elif df['12/13']['Apps'][i] > 9.0:
        x += 1
    if x > 2:
        array.append(True)
        x = 0
    else:
        array.append(False)
        x = 0

df[array]