Pandas categorical variable with missing data
Suppose that I have this dataframe:
dfdic = {"col1": ['azul', 'amarillo', 'amarillo', np.nan], "col2": [4, 5, 8, 10]}
df = pd.DataFrame(dfdic)
I want to convert the col1 field to dummy variables. I can do that by:
pd.get_dummies(df, columns=['col1']).head()
which gives
col2 col1_amarillo col1_azul
0 4.0 0 1
1 5.0 1 0
2 8.0 1 0
3 10 0 0
The NaN in col1 has been replaced by two zeroes in the dummy variables. This makes sense because it is saying that the instance does not belong to any of the categories. However, how can I replace those zeroes by NaNs, so I could have
col2 col1_amarillo col1_azul
0 4.0 0 1
1 5.0 1 0
2 8.0 1 0
3 10 NaN NaN
python pandas missing-data
asked Nov 15 '18 at 21:54
Vladimir VargasVladimir Vargas
483621
add a comment |
Suppose that I have this dataframe:
dfdic = {"col1": ['azul', 'amarillo', 'amarillo', np.nan], "col2": [4, 5, 8, 10]}
df = pd.DataFrame(dfdic)
I want to convert the col1 field to dummy variables. I can do that by:
pd.get_dummies(df, columns=['col1']).head()
which gives
col2 col1_amarillo col1_azul
0 4.0 0 1
1 5.0 1 0
2 8.0 1 0
3 10 0 0
The NaN in col1 has been replaced by two zeroes in the dummy variables. This makes sense because it is saying that the instance does not belong to any of the categories. However, how can I replace those zeroes by NaNs, so I could have
col2 col1_amarillo col1_azul
0 4.0 0 1
1 5.0 1 0
2 8.0 1 0
3 10 NaN NaN
python pandas missing-data
asked Nov 15 '18 at 21:54
Vladimir VargasVladimir Vargas
483621
Ifdf2is your df with dummies,df2[df2["col2"].isna()] = np.nan?
– Evan
Nov 15 '18 at 22:01
add a comment |
Suppose that I have this dataframe:
dfdic = {"col1": ['azul', 'amarillo', 'amarillo', np.nan], "col2": [4, 5, 8, 10]}
df = pd.DataFrame(dfdic)
I want to convert the col1 field to dummy variables. I can do that by:
pd.get_dummies(df, columns=['col1']).head()
which gives
col2 col1_amarillo col1_azul
0 4.0 0 1
1 5.0 1 0
2 8.0 1 0
3 10 0 0
The NaN in col1 has been replaced by two zeroes in the dummy variables. This makes sense because it is saying that the instance does not belong to any of the categories. However, how can I replace those zeroes by NaNs, so I could have
col2 col1_amarillo col1_azul
0 4.0 0 1
1 5.0 1 0
2 8.0 1 0
3 10 NaN NaN
python pandas missing-data
asked Nov 15 '18 at 21:54
Vladimir VargasVladimir Vargas
483621
Suppose that I have this dataframe:
dfdic = {"col1": ['azul', 'amarillo', 'amarillo', np.nan], "col2": [4, 5, 8, 10]}
df = pd.DataFrame(dfdic)
I want to convert the col1 field to dummy variables. I can do that by:
pd.get_dummies(df, columns=['col1']).head()
which gives
col2 col1_amarillo col1_azul
0 4.0 0 1
1 5.0 1 0
2 8.0 1 0
3 10 0 0
The NaN in col1 has been replaced by two zeroes in the dummy variables. This makes sense because it is saying that the instance does not belong to any of the categories. However, how can I replace those zeroes by NaNs, so I could have
col2 col1_amarillo col1_azul
0 4.0 0 1
1 5.0 1 0
2 8.0 1 0
3 10 NaN NaN
python pandas missing-data
python pandas missing-data
asked Nov 15 '18 at 21:54
Vladimir VargasVladimir Vargas
483621
asked Nov 15 '18 at 21:54
Vladimir VargasVladimir Vargas
483621
edited Nov 15 '18 at 22:30
Vladimir Vargas
asked Nov 15 '18 at 21:54
Vladimir VargasVladimir Vargas
483621
asked Nov 15 '18 at 21:54
Vladimir VargasVladimir Vargas
483621
asked Nov 15 '18 at 21:54
Vladimir VargasVladimir Vargas
483621
483621
Ifdf2is your df with dummies,df2[df2["col2"].isna()] = np.nan?
– Evan
Nov 15 '18 at 22:01
add a comment |
Ifdf2is your df with dummies,df2[df2["col2"].isna()] = np.nan?
– Evan
Nov 15 '18 at 22:01
If
df2 is your df with dummies, df2[df2["col2"].isna()] = np.nan?– Evan
Nov 15 '18 at 22:01
If
df2 is your df with dummies, df2[df2["col2"].isna()] = np.nan?– Evan
Nov 15 '18 at 22:01
add a comment |
1 Answer
1
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votes
mask + isnull
You can use mask to make selected columns null dependent on another series.
df.iloc[:, 1:] = df.iloc[:, 1:].mask(df['col2'].isnull())
print(df)
col2 col1_amarillo col1_azul
0 4.0 0.0 1.0
1 5.0 1.0 0.0
2 8.0 1.0 0.0
3 NaN NaN NaN
answered Nov 15 '18 at 22:01
jppjpp
102k2165115
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
mask + isnull
You can use mask to make selected columns null dependent on another series.
df.iloc[:, 1:] = df.iloc[:, 1:].mask(df['col2'].isnull())
print(df)
col2 col1_amarillo col1_azul
0 4.0 0.0 1.0
1 5.0 1.0 0.0
2 8.0 1.0 0.0
3 NaN NaN NaN
answered Nov 15 '18 at 22:01
jppjpp
102k2165115
add a comment |
mask + isnull
You can use mask to make selected columns null dependent on another series.
df.iloc[:, 1:] = df.iloc[:, 1:].mask(df['col2'].isnull())
print(df)
col2 col1_amarillo col1_azul
0 4.0 0.0 1.0
1 5.0 1.0 0.0
2 8.0 1.0 0.0
3 NaN NaN NaN
answered Nov 15 '18 at 22:01
jppjpp
102k2165115
add a comment |
mask + isnull
You can use mask to make selected columns null dependent on another series.
df.iloc[:, 1:] = df.iloc[:, 1:].mask(df['col2'].isnull())
print(df)
col2 col1_amarillo col1_azul
0 4.0 0.0 1.0
1 5.0 1.0 0.0
2 8.0 1.0 0.0
3 NaN NaN NaN
answered Nov 15 '18 at 22:01
jppjpp
102k2165115
mask + isnull
You can use mask to make selected columns null dependent on another series.
df.iloc[:, 1:] = df.iloc[:, 1:].mask(df['col2'].isnull())
print(df)
col2 col1_amarillo col1_azul
0 4.0 0.0 1.0
1 5.0 1.0 0.0
2 8.0 1.0 0.0
3 NaN NaN NaN
answered Nov 15 '18 at 22:01
jppjpp
102k2165115
answered Nov 15 '18 at 22:01
jppjpp
102k2165115
answered Nov 15 '18 at 22:01
jppjpp
102k2165115
answered Nov 15 '18 at 22:01
jppjpp
102k2165115
102k2165115
add a comment |
add a comment |
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If
df2is your df with dummies,df2[df2["col2"].isna()] = np.nan?– Evan
Nov 15 '18 at 22:01