Pandas won't fillna() inplace

Question

I'm trying to fill NAs with "" on 4 specific columns in a data frame that are string/object types. I can assign these columns to a new variable as I fillna(), but when I fillna() inplace the underlying data doesn't change. ``` a_n6 = a_n6[["PROV LAST", "PROV FIRST", "PROV MID", "SPEC NM"]].fillna("") a_n6 ``` gives me: ``` Int64Index: 1542 entries, 0 to 3611 Data columns (total 4 columns): PROV LAST 1542 non-null values PROV FIRST 1542 non-null values PROV MID 1542 non-null values SPEC NM 1542 non-null values dtypes: object(4) ``` but ``` a_n6[["PROV LAST", "PROV FIRST", "PROV MID", "SPEC NM"]].fillna("", inplace=True) a_n6 ``` gives me: ``` Int64Index: 1542 entries, 0 to 3611 Data columns (total 7 columns): NPI 1103 non-null values PIN 1542 non-null values PROV FIRST 1541 non-null values PROV LAST 1542 non-null values PROV MID 1316 non-null values SPEC NM 1541 non-null values flag 439 non-null values dtypes:…

Accepted Answer

Use a `dict` as the `value` argument to `fillna()`

As mentioned in the comment by @rhkarls on @Jeff's answer, using `.loc` indexed to a list of columns won't support `inplace` operations, which I too find frustrating. Here's a workaround.

Example:

```
import pandas as pd
import numpy as np

df = pd.DataFrame({'a':[1,2,3,4,np.nan],
                   'b':[6,7,8,np.nan,np.nan],
                   'x':[11,12,13,np.nan,np.nan],
                   'y':[16,np.nan,np.nan,19,np.nan]})
print(df)
#     a    b     x     y
#0  1.0  6.0  11.0  16.0
#1  2.0  7.0  12.0   NaN
#2  3.0  8.0  13.0   NaN
#3  4.0  NaN   NaN  19.0
#4  NaN  NaN   NaN   NaN
```

Let's say we want to `fillna` for `x` and `y` only, not `a` and `b`.

I would expect using `.loc` to work (as in an assignment), but it doesn't, as mentioned earlier:

```
# doesn't work
df.loc[:,['x','y']].fillna(0, inplace=True)
print(df) # nothing changed
```

However, the documentation says that the `value` argument to `fillna()` can be:

alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). (values not in the dict/Series/DataFrame will not be filled).

It turns out that using a dict of values will work:

```
# works
df.fillna({'x':0, 'y':0}, inplace=True)
print(df)
#     a    b     x     y
#0  1.0  6.0  11.0  16.0
#1  2.0  7.0  12.0   0.0
#2  3.0  8.0  13.0   0.0
#3  4.0  NaN   0.0  19.0
#4  NaN  NaN   0.0   0.0
```

Also, if you have a lot of columns in your subset, you could use a dict comprehension, as in:

```
df.fillna(dict.fromkeys(['x', 'y'], 0), inplace=True) # also works
```

Pandas won't fillna() inplace

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