Turn dictionary into dataframe

Question

I'm new to Python. I have this kind of dictionary, from a geodesic output and i wonder if i can turn this into DataFrame or matrix? here's an example data, but what i'm working right now has more than 8000 data

{(0, 0): 0.0,
 (0, 1): 1.3128088339744233,
 (1, 0): 1.3128088339744233,
 (1, 1): 0.0}

desired output would be a DataFrame that looks like this

or is there any other way to create distance matrix using geodesic from geopy.calculation?

Answer 1

Try this -

I have added additional entries to show how this approach scales to more rows and column indexes, and handles missing row, column indexes as well.

d = {(0, 0): 0.0,
     (0, 1): 1.3128088339744233,
     (1, 0): 1.3128088339744233,
     (1, 1): 0.0}

df = pd.DataFrame(d.values(), index=d.keys()).unstack(-1).droplevel(0, axis=1)
print(df)

          0         1
0  0.000000  1.312809
1  1.312809  0.000000

---

Additional test with missing rows and column indexes -

d = {(0, 0): 0.0,
     (0, 1): 1.3128088339744233,
     (1, 0): 1.3128088339744233,
     (1, 1): 0.0,
     (1, 2): 1.7,        #More entries
     (2, 1): 2.3}        #More entries

df = pd.DataFrame(d.values(), index=d.keys()).unstack(-1).droplevel(0, axis=1)
print(df)

          0         1    2
0  0.000000  1.312809  NaN
1  1.312809  0.000000  1.7
2       NaN  2.300000  NaN

---

Alternate way -

df = pd.DataFrame.from_dict(d, orient='index')
df.index = pd.MultiIndex.from_tuples(df.index)
df = df.unstack(-1).droplevel(0, axis=1)

Answer 2

Firstly, install pandas

import pandas as pd

Make use of pd.DataFrame.from_dict() function. It has two arguments you need. Columns which specify your column name and orient for row. So go through dictionary key and assign first element as column(row) names and second as row(column).

Source is here

Answer 3

Use this:

dicti = {(0, 0): 0.0,
 (0, 1): 1.3128088339744233,
 (1, 0): 1.3128088339744233,
 (1, 1): 0.0}

rows = [a[0] for a in list(dicti.keys())]
columns = [a[1] for a in list(dicti.keys())]

#only storing unique values for rows and columns
rows = list(dict.fromkeys(rows))
columns = list(dict.fromkeys(columns))

#Initializing empty dict in order to create dataframe from it
df_dict = {}
for val in columns:
  df_dict[val] = []

for k,v in dicti.items():
  df_dict[k[1]].append(v)

df = pd.DataFrame(df_dict, columns=columns, index=rows) #creating DataFrame

df