How to concatenate two frames with different number of columns in pandas?

Question

I have the following Dataframes:

Dataframe 1:

|---------------------|------------------|
|      property_id    |        beds      |
|---------------------|------------------|
|          1          |         1        |
|---------------------|------------------|
|          2          |         2        | 
|---------------------|------------------|

Dataframe 2:

|---------------------| 
|      property_id    |
|---------------------|
|          3          |
|---------------------|
|          4          |
|---------------------|

What I want to produce is the following Dataframe:

|---------------------|------------------|
|      property_id    |        beds      |
|---------------------|------------------|
|          1          |         1        |
|---------------------|------------------|
|          2          |         2        | 
|---------------------|------------------|
|          3          |         0        |
|---------------------|------------------|
|          4          |         0        | 
|---------------------|------------------|

What I want is to concatenate two Dataframes, and the former has more columns than the latter, but all the columns of the latter are in the former. When the column is not present in the latter dataframe I want to set a default value of 0. How can I achieve this?

df1 = pd.DataFrame({'property_id': [1, 2], 'beds': [1, 2]})
df2 = pd.DataFrame({'property_id': [3, 4]})

I have almost no experience with pandas, so what could I do?

Answer 1

You can use pandas.concat or append method for this, both methods will generate NA for columns that don't exist in the sub data frame, to fill them with zero, you can use fillna(0):

df1.append(df2).fillna(0)

#  beds     property_id
#0  1.0          1
#1  2.0          2
#0  0.0          3
#1  0.0          4


pd.concat([df1, df2]).fillna(0)

#  beds     property_id
#0  1.0         1
#1  2.0         2
#0  0.0         3
#1  0.0         4

Answer 2

df1.append(df2.reindex_axis(df1.columns, 1, fill_value=0))

The advantage is that integer types should be preserved