Transform Dic to dataframe

[d9d9d]

I am trying to transform this kind of dic to classic dataframe.

list = [
{
"Fruit": [{"XY": 15.2,22,22 },
{"XX": 19, 23,23},
{"XXX": 17.8,24,24}
]
}

To output like this

XY XX XXX
0 15.2 19 17.8
1 22 23 24
2 22 23 24

How is it possible?

[deanhystad]

df =  pd.DataFrame({"XY": [15.2,22,22], "XX": [19, 23,23], "XXX": [17.8,24,24]})
print(df)

Output:
     XY  XX   XXX
0  15.2  19  17.8
1  22.0  23  24.0
2  22.0  23  24.0

I don't understand your data. Maybe it should be like this:

foods = {  # Do not use "list" as a variable name.  "list" is the name of a built-in function.
    "Fruit":{"XY": [15.2,22,22], "XX": [19, 23,23], "XXX": [17.8,24,24]}
}
df =  pd.DataFrame(foods)
print(df)

Output:
              Fruit
XX     [19, 23, 23]
XXX  [17.8, 24, 24]
XY   [15.2, 22, 22]

[d9d9d]

df =  pd.DataFrame({"XY": [15.2,22,22], "XX": [19, 23,23], "XXX": [17.8,24,24]})
print(df)

Output:
     XY  XX   XXX
0  15.2  19  17.8
1  22.0  23  24.0
2  22.0  23  24.0

I don't understand your data. Maybe it should be like this:

foods = {  # Do not use "list" as a variable name.  "list" is the name of a built-in function.
    "Fruit":{"XY": [15.2,22,22], "XX": [19, 23,23], "XXX": [17.8,24,24]}
}
df =  pd.DataFrame(foods)
print(df)

Output:
              Fruit
XX     [19, 23, 23]
XXX  [17.8, 24, 24]
XY   [15.2, 22, 22]

Many thanks for reply but now i am getting this kind of error

If using all scalar values, you must pass an index

[Larz60+]

recheck your code, deanhystad's solution works for me.

Did you remember to change the name of your first list?
You cannot use a reserved word.

[perfringo]

To output like this

XY XX XXX
0 15.2 19 17.8
1 22 23 24
2 22 23 24

How is it possible?

This is not possible. Columns in pandas are series and must have same datatype. So you can't have column which has floats and integers in it. Adding a float value to an integer series will turn the whole series values to float (adding a string to a numeric series will force the series to object datatype, but the main benefit of Pandas, i.e. vectorized computations, is lost as soon as you start using object series).

As @deanhystad also pointed out, source data provided is not valid Python. So one needs to make assumptions what the real data is. My interpretation/assumption of the data and the desired output:

import pandas as pd

record = [{"Fruit": [{"XY": [15.2,22,22]},
                     {"XX": [19, 23,23]},
                     {"XXX": [17.8,24,24]}]}]

table = {key: value for item in record[0]["Fruit"] for key, value in item.items()}
df = pd.DataFrame(table)
print(df)

Output:
     XY  XX   XXX
0  15.2  19  17.8
1  22.0  23  24.0
2  22.0  23  24.0