Add column to pandas df depending on a condition statement - pandas

Question

I have a df called lin_reg_df with two columns Surface_Elevation_mAHD and Adopted_SS_WL.
The df holds measurements of each for 88 groundwater wells that each have a particular well name.
lin_reg_df is indexed by well name.
I want to add another column to the df that is called Aquifer_Type and specifies if the well is deep or shallow.
The well names of all the deep wells are held in a list called deep_wells and shallow wells are held in shallow_wells
I want to cycle through the well names (index of the df) and if the well name is listed in the
list called deep_wells I want to put a deep in the Aquifer_Type column. If it is listed
in the list called shallow_wells I want to put a shallow in the Aquifer_Type column.
I tried using isin within the loop but I couldnt get it to work.
Any advice?`

Answer 1

I tried to create a minimum example from your information, with following code:
well_name = ['a','b','c','d','e','f','g']
deep_wells = ['a','c','g']
shallow_wells = ['b','d','e','f']
lin_reg_df = pd.DataFrame({'Surface_Elevation_mAHD ': [1,2,3,4,5,6,7],
'Adopted_SS_WL': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]})
lin_reg_df.index = well_names
so my DataFrame initially looks like this:
Surface_Elevation_mAHD Adopted_SS_WL
a 1 0.1
b 2 0.2
c 3 0.3
d 4 0.4
e 5 0.5
f 6 0.6
g 7 0.7
I would then just use this code snippet to do the job:
for well in well_name:
if well in deep_wells:
lin_reg_df.loc[well, 'Aquifier_Type'] = 'deep'
elif well in shallow_wells:
lin_reg_df.loc[well, 'Aquifier_Type'] = 'shallow'
The output will be:
Surface_Elevation_mAHD Adopted_SS_WL Aquifier_Type
a 1 0.1 deep
b 2 0.2 shallow
c 3 0.3 deep
d 4 0.4 shallow
e 5 0.5 shallow
f 6 0.6 shallow
g 7 0.7 deep

Answer 2

Always give data to get help faster
Data
df=pd.DataFrame({'Surface_Elevation_mAHD':[3,4,6,7.8,9,2,5],'Adopted_SS_WL':[1,2,3,4,5,6,7],'well name':['WQ','RT','KL','SZ','TR','YH','YP']})
df
Lists
deep_wells=['WQ','RT','YH','YP']
shallow_wells=['KL','SZ','TR']
One other way is to use np.where for numpy.where(condition,yes,no)
df['Aquifer_Type']= np.where(df['well name'].isin(shallow_wells), 'shallow_wells', 'deep_wells')
df
If your well name is the index. Please reset it before applying np.wehere. You do that by df.reset_index(inplace=True)