子集pandas数据帧并保留原始大小

Question

I am trying to subset a dataframe but want the new dataframe to have same size of original dataframe.
Attaching the input, output and the expected output.

df_input = pd.DataFrame([[1,2,3,4,5], [2,1,4,7,6], [5,6,3,7,0]], columns=["A", "B","C","D","E"])

df_output=pd.DataFrame(df_input.iloc[1:2,:])

df_expected_output=pd.DataFrame([[0,0,0,0,0], [2,1,4,7,6], [0,0,0,0,0]], columns=["A", "B","C","D","E"])  

Please suggest the way forward.

Answer 1

Set the index after you subset back to the original with reindex . This will set all the values for the new rows to NaN , which you can replace with 0 via fillna . Since NaN is a floa t type, you can convert everything back to int with astype .

 df_input.iloc[1:2,:].reindex(df_input.index).fillna(0).astype(int)

Answer 2

Setup

df = pd.DataFrame([[1,2,3,4,5], [2,1,4,7,6], [5,6,3,7,0]], columns=["A", "B","C","D","E"])
output = df_input.iloc[1:2,:]

---

You can create a mask and use multiplication:

m = df.index.isin(output.index)
m[:, None] * df

   A  B  C  D  E
0  0  0  0  0  0
1  2  1  4  7  6
2  0  0  0  0  0

Answer 3

I will using where + between

df_input.where(df_input.index.to_series().between(1,1),other=0)
Out[611]: 
   A  B  C  D  E
0  0  0  0  0  0
1  2  1  4  7  6
2  0  0  0  0  0

Answer 4

One more option would be to create DataFrame with zero values and then update it with df_input slice

df_output = pd.DataFrame(0, index=df_input.index, columns = df_input.columns)
df_output.update(df_input.iloc[1:2,:])