python pandas dataframe到字典

I’ve a two columns dataframe, and intend to convert it to python dictionary – the first column will be the key and the second will be the value. Thank you in advance.

Dataframe:

    id    value
0    0     10.2
1    1      5.7
2    2      7.4

See the docs for to_dict. You can use it like this:

df.set_index('id').to_dict()

And if you have only one column, to avoid the column name is also a level in the dict (actually, in this case you use the Series.to_dict()):

df.set_index('id')['value'].to_dict()

mydict = dict(zip(df.id, df.value))

If you want a simple way to preserve duplicates, you could use groupby:

>>> ptest = pd.DataFrame([['a',1],['a',2],['b',3]], columns=['id', 'value']) 
>>> ptest
  id  value
0  a      1
1  a      2
2  b      3
>>> {k: g["value"].tolist() for k,g in ptest.groupby("id")}
{'a': [1, 2], 'b': [3]}

The answers by joris in this thread and by punchagan in the duplicated thread are very elegant, however they will not give correct results if the column used for the keys contains any duplicated value.

For example:

>>> ptest = p.DataFrame([['a',1],['a',2],['b',3]], columns=['id', 'value']) 
>>> ptest
  id  value
0  a      1
1  a      2
2  b      3

# note that in both cases the association a->1 is lost:
>>> ptest.set_index('id')['value'].to_dict()
{'a': 2, 'b': 3}
>>> dict(zip(ptest.id, ptest.value))
{'a': 2, 'b': 3}

If you have duplicated entries and do not want to lose them, you can use this ugly but working code:

>>> mydict = {}
>>> for x in range(len(ptest)):
...     currentid = ptest.iloc[x,0]
...     currentvalue = ptest.iloc[x,1]
...     mydict.setdefault(currentid, [])
...     mydict[currentid].append(currentvalue)
>>> mydict
{'a': [1, 2], 'b': [3]}

Simplest solution:

df.set_index('id').T.to_dict('records')

Example:

df= pd.DataFrame([['a',1],['a',2],['b',3]], columns=['id','value'])
df.set_index('id').T.to_dict('records')

If you have multiple values, like val1, val2, val3,etc and u want them as lists, then use the below code:

df.set_index('id').T.to_dict('list')

in some versions the code below might not work

mydict = dict(zip(df.id, df.value))

so make it explicit

id_=df.id.values
value=df.value.values
mydict=dict(zip(id_,value))

Note i used id_ because the word id is reserved word

You can use ‘dict comprehension’

my_dict = {row[0]: row[1] for row in df.values}

Another (slightly shorter) solution for not losing duplicate entries:

>>> ptest = pd.DataFrame([['a',1],['a',2],['b',3]], columns=['id','value'])
>>> ptest
  id  value
0  a      1
1  a      2
2  b      3

>>> pdict = dict()
>>> for i in ptest['id'].unique().tolist():
...     ptest_slice = ptest[ptest['id'] == i]
...     pdict[i] = ptest_slice['value'].tolist()
...

>>> pdict
{'b': [3], 'a': [1, 2]}

You need a list as a dictionary value. This code will do the trick.

from collections import defaultdict
mydict = defaultdict(list)
for k, v in zip(df.id.values,df.value.values):
    mydict[k].append(v)

I found this question while trying to make a dictionary out of three columns of a pandas dataframe. In my case the dataframe has columns A, B and C (let’s say A and B are the geographical coordinates of longitude and latitude and C the country region/state/etc, which is more or less the case).

I wanted a dictionary with each pair of A,B values (dictionary key) matching the value of C (dictionary value) in the corresponding row (each pair of A,B values is guaranteed to be unique due to previous filtering, but it is possible to have the same value of C for different pairs of A,B values in this context), so I did:

mydict = dict(zip(zip(df['A'],df['B']), df['C']))

Using pandas to_dict() also works:

mydict = df.set_index(['A','B']).to_dict(orient='dict')['C']

(none of the columns A or B were used as index before executing the line creating the dictionary)

Both approaches are fast (less than one second on a dataframe with 85k rows, 5-year-old fast dual-core laptop).

The reasons I’m posting this:

1. for those who need this kind of solution
2. if someone knows a faster executing solution (e.g., for millions of rows), I’d appreciate a reply.

def get_dict_from_pd(df, key_col, row_col):
    result = dict()
    for i in set(df[key_col].values):
        is_i = df[key_col] == i
        result[i] = list(df[is_i][row_col].values)
    return result

this is my sloution, a basic loop

This is my solution:

import pandas as pd
df = pd.read_excel('dic.xlsx')
df_T = df.set_index('id').T
dic = df_T.to_dict('records')
print(dic)