Use dictionary data to append data to pandas dataframe
IIUC, the keys in your two dictionaries are aligned. One way is to create a dataframe with a column id containing the values in dict_1 and 2 (in this case but can be more) columns from the values in dict_2 aligned on the same key. Then use merge on id to get the result back in df
# the two dictionaries. note in dict_2 I added an element for the list in key 2
# to show it works for any number of columns
dict_1 = {1:['a1', 'a3'],2:['a2', 'a4'],}
dict_2 = {1:[0,1],2:[1,1,2]}
#create a dataframe from dict_2, here it might be something easier but can't find it
df_2 = pd.concat([pd.Series(vals, name=key)
for key, vals in dict_2.items()], axis=1).T
print(df_2) #index are the keys, and columns are the future new_col_x
0 1 2
1 0.0 1.0 NaN
2 1.0 1.0 2.0
#concat with the dict_1 once explode the values in the list,
# here just a print to see what it's doing
print (pd.concat([pd.Series(dict_1, name='id').explode(),df_2], axis=1))
id 0 1 2
1 a1 0.0 1.0 NaN
1 a3 0.0 1.0 NaN
2 a2 1.0 1.0 2.0
2 a4 1.0 1.0 2.0
# use previous concat, with a rename to change column names and merge to df
df = df.merge(pd.concat([pd.Series(dict_1, name='id').explode(),df_2], axis=1)
.rename(columns=lambda x: f'new_col_{x+1}'
if isinstance(x, int) else x),
on='id', how='left')
and you get
print (df)
col_1 col_2 id col_3 new_col_1 new_col_2 new_col_3
0 100 500 a1 478 0.0 1.0 NaN
1 785 400 a1 490 0.0 1.0 NaN
2 ... ... a1 ... 0.0 1.0 NaN
3 ... ... a2 ... 1.0 1.0 2.0
4 ... ... a2 ... 1.0 1.0 2.0
5 ... ... a2 ... 1.0 1.0 2.0
6 ... ... a3 ... 0.0 1.0 NaN
7 ... ... a3 ... 0.0 1.0 NaN
8 ... ... a3 ... 0.0 1.0 NaN
9 ... ... a4 ... 1.0 1.0 2.0
10 ... ... a4 ... 1.0 1.0 2.0
11 ... ... a4 ... 1.0 1.0 2.0
Let us try explode with map
s=pd.Series(dict_1).explode().reset_index()
s.columns=[1,2]
df['new_1']=df.id.map(dict(zip(s[2],s[1])))
#s=pd.Series(dict_2).explode().reset_index()
#s.columns=[1,2]
#df['new_2']=df.id.map(dict(zip(s[2],s[1])))
Assume you have 'n values from the lists of dict_2 and want to construct n new columns in df' such as
dict_2 = {1: [0, 1], 2: [1, 1, 6, 9]}
Using dict comprehension to construct a new dictionary from dict_2 and dict_1 and use it to construct a new dataframe with orient='index'. Chaining rename and add_prefix. Finally, merge it back to df with option left_on='id', right_index=True
key_dict = {x: v for k, v in dict_2.items() for x in dict_1[k]}
df_add = (pd.DataFrame.from_dict(key_dict, orient='index')
.rename(lambda x: int(x)+1, axis=1).add_prefix('newcol_'))
df_final = df.merge(df_add, left_on='id', right_index=True)
Out[33]:
col_1 col_2 id col_3 newcol_1 newcol_2 newcol_3 newcol_4
0 100 500 a1 478 0 1 NaN NaN
1 785 400 a1 490 0 1 NaN NaN
2 ... ... a1 ... 0 1 NaN NaN
3 ... ... a2 ... 1 1 6.0 9.0
4 ... ... a2 ... 1 1 6.0 9.0
5 ... ... a2 ... 1 1 6.0 9.0
6 ... ... a3 ... 0 1 NaN NaN
7 ... ... a3 ... 0 1 NaN NaN
8 ... ... a3 ... 0 1 NaN NaN
9 ... ... a4 ... 1 1 6.0 9.0
10 ... ... a4 ... 1 1 6.0 9.0
11 ... ... a4 ... 1 1 6.0 9.0