Как удалить последний столбец данных pandas dataframe

Question

У меня есть некоторые данные cvs, в которых есть пустой столбец в конце каждой строки. Я хотел бы оставить его вне импорта или, альтернативно, удалить его после импорта. Мои данные cvs имеют различное количество столбцов. Я пробовал использовать df.tail(), но не смог выбрать последний столбец с ним.

employment=pd.read_csv('./data/spanish/employment1976-1987thousands.csv',index_col=0,header=[7,8],encoding='latin-1') 

данных:

4.- Resultados provinciales 
Encuesta de Población Activa. Principales Resultados 

Activos por provincia y grupo de edad (4). 
Unidades:miles de personas 


,Álava,,,,Albacete,,,,Alicante,,,,Almería,,,,Asturias,,,,Ávila,,,,Badajoz,,,,Balears (Illes),,,,Barcelona,,,,Burgos,,,,Cáceres,,,,Cádiz,,,,Cantabria,,,,Castellón de la Plana,,,,Ciudad Real,,,,Córdoba,,,,Coruña (A),,,,Cuenca,,,,Girona,,,,Granada,,,,Guadalajara,,,,Guipúzcoa,,,,Huelva,,,,Huesca,,,,Jaén,,,,León,,,,Lleida,,,,Lugo,,,,Madrid,,,,Málaga,,,,Murcia,,,,Navarra,,,,Orense,,,,Palencia,,,,Palmas (Las),,,,Pontevedra,,,,Rioja (La),,,,Salamanca,,,,Santa Cruz de Tenerife,,,,Segovia,,,,Sevilla,,,,Soria,,,,Tarragona,,,,Teruel,,,,Toledo,,,,Valencia,,,,Valladolid,,,,Vizcaya,,,,Zamora,,,,Zaragoza,,,,Ceuta y Melilla,,,, 
,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años,de 16 a 19 años,de 20 a 24 años,de 25 a 54 años,de 55 y más años, 
1976TIII,"8.9","11.6","60.4","11.8","16.4","14.4","65.2","14.9","47.9","49.9","246.0","60.1","20.5","14.3","88.9","11.2","34.5","42.5","278.0","91.3","6.6","7.2","41.5","13.3","25.3","22.8","135.3","37.5","19.8","24.4","153.0","43.0","166.8","203.7","1079.0","230.7","14.1","16.4","86.0","23.8","17.0","18.3","86.6","28.6","31.0","38.7","180.4","29.8","15.3","19.2","120.6","30.4","19.9","15.3","104.2","23.4","19.7","19.5","97.5","29.7","28.0","23.9","140.5","30.1","29.1","46.1","263.8","70.0","8.9","6.2","45.7","14.6","19.7","19.7","123.0","35.3","26.8","22.5","141.0","36.2","4.8","6.0","33.1","13.4","23.1","31.6","174.5","33.8","11.9","14.3","83.8","18.8","7.0","9.3","50.3","20.0","22.4","23.4","125.8","28.6","22.7","21.6","143.1","50.9","12.5","13.7","89.5","33.2","14.3","14.7","134.0","54.7","136.6","207.5","1067.6","218.6","34.7","41.1","196.4","38.4","37.2","35.0","200.5","46.1","15.6","23.8","111.6","30.7","14.0","16.8","120.2","74.9","5.7","6.4","39.2","8.0","24.5","25.6","135.3","27.1","36.4","39.4","246.1","74.0","10.2","11.3","63.9","13.4","10.5","11.0","74.1","19.6","19.3","23.9","140.3","31.7","5.5","6.0","35.6","11.3","55.2","55.6","262.5","68.1","3.1","3.2","24.4","5.4","21.8","18.4","116.7","37.1","4.6","3.4","37.3","12.0","20.3","16.7","102.2","23.1","73.5","85.5","454.6","101.5","19.2","23.4","90.7","20.5","41.3","54.7","272.2","57.0","6.0","7.1","56.5","28.9","29.2","32.1","192.7","49.8","0.0","0.0","0.0","0.0", 
1976TIV,"8.7","11.7","60.8","11.4","14.4","13.6","63.3","14.5","49.1","50.6","244.9","54.2","19.0","16.9","86.8","11.4","33.2","42.3","271.8","86.0","5.8","7.5","40.3","13.9","25.1","24.7","132.7","38.4","18.8","23.4","151.8","43.9","172.2","201.7","1070.7","228.1","11.1","15.7","82.5","21.1","16.4","18.0","89.2","26.6","32.6","40.0","176.5","30.5","15.8","18.1","121.3","30.2","19.0","17.3","106.3","24.1","19.9","19.0","101.7","26.9","25.3","22.3","142.7","28.9","30.0","42.4","267.6","70.1","7.3","7.0","44.4","13.0","17.8","21.4","122.8","34.0","28.4","21.6","140.5","36.8","4.7","6.6","32.6","10.8","24.8","32.7","177.2","32.3","11.9","12.5","85.4","20.5","6.9","8.5","48.8","19.9","22.4","22.1","127.6","25.1","18.5","21.1","137.8","48.7","12.4","11.1","84.9","31.5","13.6","15.6","132.7","52.0","144.0","202.3","1054.0","222.5","35.6","40.1","194.1","37.5","36.7","34.7","203.8","47.1","15.6","23.6","114.3","31.3","14.0","15.9","118.3","76.7","5.5","7.3","36.9","9.3","25.5","25.1","138.7","26.8","34.8","42.9","250.3","74.9","9.9","11.8","62.8","14.0","10.0","13.2","74.5","19.2","19.5","24.2","142.7","31.0","4.0","5.9","35.5","12.0","55.0","56.7","264.7","63.3","2.8","3.5","23.9","5.1","20.0","21.6","116.4","34.9","4.5","3.7","36.5","12.1","21.1","17.6","100.6","25.7","74.6","87.5","455.5","102.1","18.9","22.9","90.0","21.6","40.2","57.1","273.9","58.5","5.6","8.3","57.6","23.9","28.3","31.4","192.2","46.4","0.0","0.0","0.0","0.0", 
1977TI,"9.2","11.8","59.9","11.2","14.2","13.2","65.9","14.7","48.2","50.4","251.1","50.8","17.8","15.4","86.5","11.8","30.6","42.9","272.6","84.1","5.8","7.4","37.2","12.8","24.1","22.8","131.3","38.2","17.8","23.5","151.1","42.5","168.1","200.4","1077.2","223.3","11.6","12.8","80.9","17.6","14.4","16.4","88.2","23.9","34.5","37.5","176.3","30.8","15.2","19.7","121.3","31.6","18.4","19.4","107.4","24.7","20.0","18.1","98.3","26.6","24.9","23.6","150.7","27.5","29.5","40.3","267.4","70.5","5.6","7.5","44.2","12.8","17.1","21.1","122.8","33.6","29.6","23.3","142.1","37.9","4.6","5.5","33.7","11.2","23.5","30.4","175.2","32.8","12.0","12.7","84.8","21.3","7.3","9.3","46.6","17.8","30.2","26.0","147.1","25.2","15.9","22.7","133.2","45.1","12.8","12.1","84.3","28.0","12.4","16.5","131.2","55.6","150.9","202.9","1065.4","223.7","36.6","44.0","194.3","39.9","36.7","31.5","196.7","45.7","14.8","22.5","115.1","29.4","11.7","17.2","114.2","75.8","5.0","7.7","38.0","9.4","24.0","26.8","143.5","27.0","35.3","43.0","247.4","73.5","9.7","12.1","61.6","13.3","9.5","11.9","73.9","18.9","20.4","26.7","143.0","31.6","4.0","5.0","35.5","12.3","52.3","58.0","266.0","62.5","2.6","2.7","24.2","6.0","17.3","21.0","113.0","33.3","4.5","5.2","33.8","10.6","18.7","18.8","98.3","24.8","77.4","87.6","446.6","100.3","20.5","23.4","90.2","20.4","38.7","50.7","277.6","57.3","6.4","8.7","60.1","21.5","28.6","31.0","194.8","45.7","0.0","0.0","0.0","0.0", 

Answer 1

Вы можете указать, какие столбцы для импорта с помощью usecols параметра для read_csv

Так или создать список имен столбцов или целочисленных значений:

cols_to_use = ['col1', 'col2'] # or [0,1,2,3] 
df = pd.read_csv('mycsv.csv', usecols= cols_to_use) 

или drop после импорта, я предпочитают прежний метод (зачем импортировать данные, которые вас не интересуют?).

df = df.drop(labels='column_to_delete', axis=1) # axis 1 drops columns, 0 will drop rows that match index value in labels 

Обратите внимание также вы поняли, что tail делает, он возвращает последние n строки (по умолчанию 5) из dataframe.

Дополнительная

Если столбцы разной длины, то вы можете просто заголовок, чтобы получить столбцы, а затем читать CSV снова правильно и падение последнего столбца:

def df_from_csv(path): 
    df = read_csv(path, nrows=1) # read just first line for columns 
    columns = df.columns.tolist() # get the columns 
    cols_to_use = columns[:len(columns)-1] # drop the last one 
    df = read_csv(path, usecols=cols_to_use) 
    return df 

Answer 2

Вот один -линер, который не требует указания имени столбца

df.drop(df.columns[len(df.columns)-1], axis=1, inplace=True) 

Answer 3

Другой способ удаления последнего столбца в DataFrame d е:

df = df.iloc[:, :-1]

Answer 4

Улучшение от @ conner.xyz ответ выше:

df.drop(df.columns[[-1,]], axis=1, inplace=True)

Если вы хотите, чтобы удалить последние два столбца, замените [-1,] на [-1, -2].