Convert Python Dict into a Dataframe

Convert Python dict into a dataframe

The error here, is since calling the DataFrame constructor with scalar values (where it expects values to be a list/dict/... i.e. have multiple columns):

pd.DataFrame(d)
ValueError: If using all scalar values, you must must pass an index

You could take the items from the dictionary (i.e. the key-value pairs):

In [11]: pd.DataFrame(d.items())  # or list(d.items()) in python 3
Out[11]:
             0    1
0   2012-07-02  392
1   2012-07-06  392
2   2012-06-29  391
3   2012-06-28  391
...

In [12]: pd.DataFrame(d.items(), columns=['Date', 'DateValue'])
Out[12]:
          Date  DateValue
0   2012-07-02        392
1   2012-07-06        392
2   2012-06-29        391

But I think it makes more sense to pass the Series constructor:

In [21]: s = pd.Series(d, name='DateValue')
Out[21]:
2012-06-08    388
2012-06-09    388
2012-06-10    388

In [22]: s.index.name = 'Date'

In [23]: s.reset_index()
Out[23]:
          Date  DateValue
0   2012-06-08        388
1   2012-06-09        388
2   2012-06-10        388

how to convert a python dict to a pandas dataframe

You can create dataframe directly, using dictionary:

my_dict2 = {"c": {"x": "aa", "y": "bb"}, "a": 1, "b": [1, 2, 3]}
df = pd.DataFrame(
    {"prop_name": my_dict2.keys(), "prop_value": my_dict2.values()}
)
print(df)

Prints:

  prop_name              prop_value
0         c  {'x': 'aa', 'y': 'bb'}
1         a                       1
2         b               [1, 2, 3]

With my_dict2 = {"a": 1, "b": [1, 2, 3], "c": {"x": "aa", "y": "bb"}} this produces:

  prop_name              prop_value
0         a                       1
1         b               [1, 2, 3]
2         c  {'x': 'aa', 'y': 'bb'}

Note: as @TrentonMcKinney said in the comments, the behaviour of how the dataframe is constructed depends on the first item of the dictionary (source):

                if isinstance(list(data.values())[0], (Series, dict)):
                    data = _from_nested_dict(data)
                else:
                    data, index = list(data.values()), list(data.keys())

so pd.DataFrame.from_dict({"b": 1, "a": [1, 2, 3]}, orient="index") succeeds and pd.DataFrame.from_dict({"a": [1, 2, 3], "b": 1},orient="index") produces an error.

converting dictionary to pandas dataframe in python

I may be missing something here but is just this what you're after?

df2 = pd.DataFrame([invoice_header])

Looks the same as df to me

How to convert python dictionary to pandas dataframe

Just get rid of those lists and you can feed directly to the DataFrame constructor:

pd.DataFrame({k: v[0] for k,v in my_dict.items()}).T

output:

         columns_1  columns_2
table_1     148989     437643
table_2    3344343    9897833

With the index as column:

(pd.DataFrame({k: v[0] for k,v in my_dict.items()})
   .T
   .rename_axis('table_name')
   .reset_index()
)

output:

  table_name  columns_1  columns_2
0    table_1     148989     437643
1    table_2    3344343    9897833

Converting dictionary into a pandas dataframe

If you using all scalar values, you must pass an index

df = pd.DataFrame({'age': '34', 'max_heart_rate': '123', 'rest_blood_pressure': '132',
     'blood_sugar': '1', 'exercice_angina': '0', 'chest_pain_type': 'typ_angina',
 'rest_electro': 'normal', 'id': 8808},index = [0])


    age max_heart_rate  rest_blood_pressure blood_sugar exercice_angina chest_pain_type rest_electro    id
0   34    123                  132              1             0    typ_angina   normal  8808

Convert Dictionary into Dataframe

Try to put the dictionary inside list ([]):

import pandas as pd

dct = {"A": [1, 2, 3], "B": [1, 2, 3, 4]}

df = pd.DataFrame([dct])
print(df)

Prints:

           A             B
0  [1, 2, 3]  [1, 2, 3, 4]

Note: Don't use reserved words such as dict for variable names.

Convert dict to pandas dataframe?

You can use json_normalize, but repeated values are in last column(s):

df = pd.json_normalize(d, 'dp', 'id')
print(df)
  Value   Key   id
0    11   abc  666
1    88  kuku  666
2    99  lulu  666
3  John  name  666

For correct ordering use:

#create list of columns dynamic - all columns names without dp
cols = [c for c in d.keys() if c != 'dp']
print(cols)
['id']

df = pd.json_normalize(d, 'dp', 'id')
#change ordering by joined lists
df = df[cols + df.columns.difference(cols, sort=False).tolist()]
print(df)
    id Value   Key
0  666    11   abc
1  666    88  kuku
2  666    99  lulu
3  666  John  name

Convert Python 3D dict into a dataframe

Use concat with DataFrame.reset_index:

return pd.concat(pd.read_excel(xl,sheet_name=sheets,index_col=False)).reset_index(drop=True)

d = {'SheetName':  pd.DataFrame({'Col1':[27,25], 'Col2':list('ab')}),
     'SheetName1':  pd.DataFrame({'Col1':[270,205], 'Col2':list('th')})} 
print (d)
{'SheetName':    Col1 Col2
0    27    a
1    25    b, 'SheetName1':    Col1 Col2
0   270    t
1   205    h}

By default get MultiIndex:

df = pd.concat(d)
print (df)
              Col1 Col2
SheetName  0    27    a
           1    25    b
SheetName1 0   270    t
           1   205    h

For convert it to column use:

df = pd.concat(d).droplevel(1).rename_axis('sheets').reset_index()
print (df)
       sheets  Col1 Col2
0   SheetName    27    a
1   SheetName    25    b
2  SheetName1   270    t
3  SheetName1   205    h

For remove MultiIndex use:

df = pd.concat(d).reset_index(drop=True)
print (df)
   Col1 Col2
0    27    a
1    25    b
2   270    t
3   205    h

Convert Python Dict into a Dataframe