df=pd.DataFrame({'dept':['dept1','dept2','dept3','dept4','dept5'],
                 'room1':['0','1','1','NA','1'],
                 'room2':['1','0','NA','1','1'],
                 'room3':['0','0','1','NA','1'],
                 'room4':['1','NA','1','1','1'],
                 'count':['4','3','3','2','4']}


        dept  room1  room2 room3  room4 count
0      dept1    0      1     0      1    4
1      dept2    1      0     0      NA   3
2      dept3    1      NA    1      1    3
3      dept4    NA     1    NA      1    2
4      dept5    1      1    1       1    4

i have a selectbox where the user can filter the required data and display records based on his selection.

expected result:

if user select dept2 :

       dept  room1  room2 room3   
0      dept2    1      0     0   

if user select dept4:

       dept    room2     room4
0      dept4     1         1

code:

option_dept = df["dept"].unique().tolist()
selected_dept = st.multiselect("search by departement",option_dept)
if selected_dept:
  df= df[df["dept"].isin(selected_dept)]

st.write(df)

the problem is that with this code all the columns are displayed

how can i remove the columns that includes NA or null in each selected row?

CodePudding user response：

Select only dept and room columns, replace possible strings NA to NaNs and remove missing columns:

df= df[df["dept"].isin(selected_dept)].filter(regex='room|dept').replace('NA', np.nan).dropna(axis=1)

Or:

df= df[df["dept"].isin(selected_dept)].drop('count', axis=1).replace('NA', np.nan).dropna(axis=1)

CodePudding user response：

Suppose the user selects dept2 then this code would give you the desired output,

pd.DataFrame(df.loc[1, :].dropna()).T

Output -

For other dept values, just change the row number in the iloc function. You can even set the indices of the dataframe to the dept value for that row using df.set_index("dept") and then use df.loc["dept_2", :] to get the data for that row.