In Python Pandas, how do I combine two columns containing strings using if/else statement or similar? - python-3.x

Question

I have created a pandas dataframe from an excel file where first two columns are:
df = pd.DataFrame({'0':['','','Location Code','pH','Ag','Alkalinity'], '1':['Lab Id','Collection Date','','','µg/L','mg/L']})
which looks like this:
df[0] df[1]
Lab Id
Collection Date
Location Code
pH
Ag µg/L
Alkalinity mg/L
I want to merge these columns into one that looks like this:
df[0]
Lab Id
Collection Date
Location Code
pH
Ag (µg/L)
Alkalinity (mg/L)
I believe I need a control statement before combining df[0] and df[1] which would appear like this:
if **there is a blank space in either column, then it performs**:
df[0] = df[0].astype(str)+df[1].astype(str)
else:
df[0] = df[0].astype(str)+' ('+df[1].astype(str)+')'
but I am not sure how to write the if statement. Could anyone please guide me here.
Thank you very much.

Answer 1

We can try np.select
cond=[(df['0']=='') & (df['1']!=''), (df['0']!='') & (df['1']==''), (df['0']!='') & (df['1'] !='')]
val=[df['1'], df['0'], df['0']+ '('+df['1']+')']
df['new']=np.select(cond,val)
df
0 1 new
0 Lab Id Lab Id
1 Collection Date Collection Date
2 Location Code Location Code
3 pH pH
4 Ag µg/L Ag(µg/L)
5 Alkalinity mg/L Alkalinity(mg/L)

Answer 2

if value is Na, maybe:
df['result'] = df[0].fillna(df[1])

Answer 3

This works using numpy where, and the string concatenation assumption is based on the data shared :
df.assign(
merger=np.where(
df["1"].str.endswith("/L"),
df["0"].str.cat(df["1"], "(").add(")"),
df["0"].str.cat(df["1"], ""),
)
)
0 1 merger
0 Lab Id Lab Id
1 Collection Date Collection Date
2 Location Code Location Code
3 pH pH
4 Ag µg/L Ag(µg/L)
5 Alkalinity mg/L Alkalinity(mg/L)
Or, you could just assign it to "0", if that is what you are after :
df["0"] = np.where(
df["1"].str.endswith("/L"),
df["0"].str.cat(df["1"], "(").add(")"),
df["0"].str.cat(df["1"], ""),
)

Answer 4

Here is another way:
First you replace values you are going to concat with the value + '()'
df['1'].loc[df.replace('', np.nan).notnull().all(axis =1 )] = '(' + df['1'] + ')'
Now we fill in missing values with bfill and ffill
df = df.replace('', np.nan).bfill(axis = 1).ffill(axis = 1)
Only thing remaining, is to merge values wherever we have brackets
df.loc[:, 'merge'] = np.where(df['1'].str.endswith(')'), df['0'] + df['1'], df['1'])

Answer 5

Test if empty value at least in one column 0,1 by DataFrame.eq and DataFrame.any and then join both columns like in your answer in numpy.where:
df = pd.DataFrame({0:['','','Location Code','pH','Ag','Alkalinity'],
1:['Lab Id','Collection Date','','',u'µg/L','mg/L']})
print (df[[0,1]].eq(''))
0 1
0 True False
1 True False
2 False True
3 False True
4 False False
5 False False
print (df[[0,1]].eq('').any(axis=1))
0 True
1 True
2 True
3 True
4 False
5 False
dtype: bool
df[0] = np.where(df[[0,1]].eq('').any(axis=1),
df[0].astype(str)+df[1].astype(str),
df[0].astype(str)+' ('+df[1].astype(str)+')')
print (df)
0 1
0 Lab Id Lab Id
1 Collection Date Collection Date
2 Location Code
3 pH
4 Ag (µg/L) µg/L
5 Alkalinity (mg/L) mg/L