How to append a dictionary with multiple keys to a dataframe-CodePudding

I am trying to append a dictionary to my DataFrame.

This is how the DataFrame looks:

NR   RS   BP
0471 10   11.41
0652 10   11.50
0650 20   11.35
6519 40   11.06

And this is the dictionary:

bpDict = {"nr":["0471","0652","0650","6519"],
          "bp2":[11.39,11.47,nan,11.07],
          "bp3":[11.43,11.46,11.33,11.08],
          "bp4":[11.44,11.44,11.37,nan]}

I need to append this dictionary to my df. I know how to do it when I append one column (using map) but this is not going to work here.

Any ideas on how to append this?

CodePudding user response：

You can use the merge() method from Pandas dataframes. You can do something like that :

df = pd.DataFrame({
    "nr":["0471","0652","0650","6519"],
    "RS":[10,10,20,40],
    "BP":[11.41, 11.50, 11.35, 11.06],
})
bpDict = {"nr":["0471","0652","0650","6519"],
          "bp2":[11.39,11.47,11.38,11.07],
          "bp3":[11.43,11.46,11.33,11.08],
          "bp4":[11.44,11.44,11.37,11.11]}
df_2 = pd.DataFrame(bpDict)
df.merge(df_2, on="nr")

CodePudding user response：

NaNs work just fine...

from numpy import nan

data = {"nr":["0471","0652","0650","6519"],
          "bp2":[11.39,11.47,nan,11.07],
          "bp3":[11.43,11.46,11.33,11.08],
          "bp4":[11.44,11.44,11.37,nan]}
df2 = pd.DataFrame(data)

out = df.merge(df2, left_on='NR', right_on='nr')
print(out)

Output:

     NR  RS     BP    nr    bp2    bp3    bp4
0  0471  10  11.41  0471  11.39  11.43  11.44
1  0652  10  11.50  0652  11.47  11.46  11.44
2  0650  20  11.35  0650    NaN  11.33  11.37
3  6519  40  11.06  6519  11.07  11.08    NaN