How to delete the last column of data of a pandas dataframe
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') 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", pandas.DataFrame.drop#
Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide for more information about the now unused levels.
Parameters labels single label or list-like
Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like.
Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).
index single label or list-like
Alternative to specifying axis ( labels, axis=0 is equivalent to index=labels ).
columns single label or list-like
Alternative to specifying axis ( labels, axis=1 is equivalent to columns=labels ).
level int or level name, optional
For MultiIndex, level from which the labels will be removed.
inplace bool, default False
If False, return a copy. Otherwise, do operation inplace and return None.
errors , default ‘raise’
If ‘ignore’, suppress error and only existing labels are dropped.
Returns DataFrame or None
DataFrame without the removed index or column labels or None if inplace=True .
If any of the labels is not found in the selected axis.
Label-location based indexer for selection by label.
Return DataFrame with labels on given axis omitted where (all or any) data are missing.
Return DataFrame with duplicate rows removed, optionally only considering certain columns.
Return Series with specified index labels removed.
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4), . columns=['A', 'B', 'C', 'D']) >>> df A B C D 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11
>>> df.drop(['B', 'C'], axis=1) A D 0 0 3 1 4 7 2 8 11
>>> df.drop(columns=['B', 'C']) A D 0 0 3 1 4 7 2 8 11
>>> df.drop([0, 1]) A B C D 2 8 9 10 11
Drop columns and/or rows of MultiIndex DataFrame
>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'], . ['speed', 'weight', 'length']], . codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], . [0, 1, 2, 0, 1, 2, 0, 1, 2]]) >>> df = pd.DataFrame(index=midx, columns=['big', 'small'], . data=[[45, 30], [200, 100], [1.5, 1], [30, 20], . [250, 150], [1.5, 0.8], [320, 250], . [1, 0.8], [0.3, 0.2]]) >>> df big small lama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 weight 1.0 0.8 length 0.3 0.2
Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination ‘falcon’ and ‘weight’ , which deletes only the corresponding row
>>> df.drop(index=('falcon', 'weight')) big small lama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 length 0.3 0.2
>>> df.drop(index='cow', columns='small') big lama speed 45.0 weight 200.0 length 1.5 falcon speed 320.0 weight 1.0 length 0.3
>>> df.drop(index='length', level=1) big small lama speed 45.0 30.0 weight 200.0 100.0 cow speed 30.0 20.0 weight 250.0 150.0 falcon speed 320.0 250.0 weight 1.0 0.8