How to merge two column of a dataframe-CodePudding

I have this kind of dataframe :

data = {'geom1': [17.45, np.nan, 17.445, np.nan], 'geom2': [np.nan,17.45,np.nan,17.455]}
df = pd.DataFrame(data)

I would like to merge the two column of the dataframe

Result expected :

data2 = {'geom': [17.45, 17.45, 17.445, 17.455]}
df_expected = pd.DataFrame(data2)

How should I proceed ?

CodePudding user response：

You can use the lambda function like this

df['geom'] = df.apply(lambda x: x['geom1'] if np.isnan(x['geom2']) else x['geom2'],axis=1)

df_expected = df[['geom']]

CodePudding user response：

if you expect there always to be exactly one column that is non-nullish, you can do:

df.geom1.fillna(df.geom2).to_frame(name='geom')

CodePudding user response：

By using sum over axis=1:

df['geom'] = df.sum(axis = 1)
print(df.head())

Output:

    geom1   geom2    geom
0  17.450     NaN  17.450
1     NaN  17.450  17.450
2  17.445     NaN  17.445
3     NaN  17.455  17.455

CodePudding user response：

df1 = pd.DataFrame(data['geom1'], columns=['geom1'])
df2 = pd.DataFrame(data['geom2'], columns=['geom2'])
df = DataFrame()
df['geom'] = pd.concat([df1, df2], axis=1).stack().values

df = pd.DataFrame()
for k,v in data.items():
    df[k] = v

df['geom'] = df.stack().values
df = df.drop(columns=[k for k in data.keys()])

df = pd.DataFrame([i for sublist in data.values() for i in sublist], columns=['geom1'])

CodePudding user response：

df.geom1.fillna(0)   df.geom2.fillna(0)

df.fillna(0).sum(axis = 1)