如何删除熊猫数据框的最后一列数据

Question

I have some cvs data that has an empty column at the end of each row. I would like to leave it out of the import or alternatively delete it after import. My cvs data's have a varying number of columns. I've tried using df.tail() , but haven't managed to choose the last column with it.

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

Data:

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

这是一个不需要指定列名的单行

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

Answer 2

删除 DataFrame df 中最后一列的另一种方法：

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

Answer 3

You can specify which columns to import using usecols parameter for read_csv

So either create a list of column names or integer values:

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

or drop the column after importing, I prefer the former method (why import data you are not interested in?).

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

Note also you misunderstand what tail does, it returns the last n rows (default is 5) of a dataframe.

Additional

If the columns are varying length then you can just the header to get the columns and then read the csv again properly and drop the last column:

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 4

Improve from @conner.xyz answer above:

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

If you want to delete the last two columns, replace [-1,] by [-1, -2] .

Answer 5

删除最后一列的另一种方法：

df = df[df.columns[:-1]]

Answer 6

导入数据后，您可以删除最后一列，无论它是什么：

employment = employment.drop(columns = employment.columns[-1])

Answer 7

与 Python 中所有基于索引的操作一样，您可以使用 -1 从末尾开始。

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

Answer 8

Just to complete the accepted answer, if you have one dataframe with only two columns, and these two columns have the same name, be aware. First you need to rename one column and then drop the desirable column.

*Edited after comment below