Return Empty column if column does not exist in dataframe

Question

I created a dataframe df as below:

Type = ['A', 'B', 'C', 'D']
Size = [72,23,66,12]
df = pd.DataFrame({'Type': Type, 'Size': Size})

I can extract any existing column using:

df_count = df['Size']

However, if a data frame is too big, and I don't know if the column exist in df or not. In such if I call a column e.g. df['Shape'] as below:

df_null = df['Shape']

It raises a KeyError. However I want that df_null should get an empty column with name "Shape".

Answer 1

Use DataFrame.get in a pattern similar to:

In [3]: df.get('Size', pd.Series(index=df.index, name='Size'))
Out[3]:
0    72
1    23
2    66
3    12
Name: Size, dtype: int64

In [4]: df.get('Shape', pd.Series(index=df.index, name='Shape'))
Out[4]:
0   NaN
1   NaN
2   NaN
3   NaN
Name: Shape, dtype: float64

Or generalize by creating a function to abstract this:

In [5]: get_column = lambda df, col: df.get(col, pd.Series(index=df.index, name=col))

In [6]: get_column(df, 'Size')
Out[6]:
0    72
1    23
2    66
3    12
Name: Size, dtype: int64

In [7]: get_column(df, 'Shape')
Out[7]:
0   NaN
1   NaN
2   NaN
3   NaN
Name: Shape, dtype: float64

Another alternative could be to use reindex and squeeze:

In [8]: df.reindex(columns=['Size']).squeeze()
Out[8]:
0    72
1    23
2    66
3    12
Name: Size, dtype: int64

In [9]: df.reindex(columns=['Shape']).squeeze()
Out[9]:
0   NaN
1   NaN
2   NaN
3   NaN
Name: Shape, dtype: float64

Answer 2

IIUC, try this

col = 'Shape'
df_null = pd.Series() if col not in df.columns else df[col]

Output:

Series([], dtype: float64)

OR

col = 'Size'
df_null = pd.Series() if col not in df.columns else df[col]

Output:

0    72
1    23
2    66
3    12
Name: Size, dtype: int64