Converting Excel to JSON using Pandas in Python 3.9

Question

this is my first ever post here so go easy! :) I am attempting to convert data from Excel to JSON using the Python Pandas library.

I have data in Excel that looks like the table below, the columns detailed as "Unnamed: x" are blank, I used these headers as that's how they are output when converting to JSON. There are around 20 tests formatted like the sample below:

Unnamed: 1	Unnamed: 2	Unnamed: 3	Unnamed: 4


Test 1	Menu	Setting	Value
	Menu1	Setting1	Value1


Test 2	A	B	C
	1	2	3

I would like to put these in to JSON to look something like this:

{
    "Test 1": [ 
    "Menu":"Menu1",
    "Setting":"Setting1",
    "Value":"Value1",
    ]
}

And so on...

I can convert the current code to JSON (but not the format detailed above, and I have been experimenting with creating different Pandas dataframes in Python. At the moment the JSON data I get looks something like this:

"3":[
        {
            "Unnamed: 0":"Test1",
            "Unnamed: 1":"Menu",
            "Unnamed: 2":"Setting",
            "Unnamed: 2":"Value"
        }
"4":[
        {
            "Unnamed: 1":"Menu1",
            "Unnamed: 2":"Setting1",
            "Unnamed: 2":"Value1"
        }

So I am doing some manual work (copying and pasting) to set it up in the desired format.

Here is my current code:

import pandas

# Pointing to file location and specifying the sheet name to convert
excel_data_fragment = pandas.read_excel('C:\\Users\\user_name\\tests\\data.xls', sheet_name='Tests')

# Converting to data frame
df = pandas.DataFrame(excel_data_fragment)

# This will get the values in Column A and removes empty values
test_titles = df['Unnamed: 0'].dropna(how="all")

# This is the first set of test values
columnB = df['Unnamed: 1'].dropna(how="all")

# Saving original data in df and removing rows which contain all NaN values to mod_df
mod_df = df.dropna(how="all")

# Converting data frame with NaN values removed to json
df_json = mod_df.apply(lambda x: [x.dropna()], axis=1).to_json()

print(mod_df)

Could you give an example of the output you would like to achieve? — Linden
– Linden, Commented May 20, 2021 at 12:09
Thanks for the reply Linden, the output I'd like to achieve is the JSON format: { "Test 1": [ "Menu":"Menu1", "Setting":"Setting1", "Value":"Value1", ] } — pcal
– pcal, Commented May 20, 2021 at 12:15

Roméo Després · Accepted Answer · 2021-05-21 11:48:41Z

Your Excel sheet is basically composed of several distinct subtables put together (one for each test). The way I would go to process them in pandas would be to use groupby and then process each group as a table. DataFrame.to_dict will be your friend here to output JSON-able objects.

First here is some sample data that ressembles what you have provided:

import pandas as pd


rows = [
    [],
    [],
    ["Test 1", "Menu", "Setting", "Value"],
    [None, "Menu1", "Setting1", "Value1"],
    [None, "Menu2", "Setting2", "Value2"],
    [],
    [],
    ["Test 2", "A", "B", "C"],
    [None, 1, 2, 3],
    [None, 4, 5, 6],
]

df = pd.DataFrame(rows, columns=[f"Unnamed: {i}" for i in range(1, 5)])

df looks like:

  Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4
0       None       None       None       None
1       None       None       None       None
2     Test 1       Menu    Setting      Value
3       None      Menu1   Setting1     Value1
4       None      Menu2   Setting2     Value2
5       None       None       None       None
6       None       None       None       None
7     Test 2          A          B          C
8       None          1          2          3
9       None          4          5          6

Then use the following snippet, which cleans up all the missing values in df and turns each subtable into a dict.

# Remove entirely empty rows
df = df.dropna(how="all")

# Fill missing values in column 1
df["Unnamed: 1"] = df["Unnamed: 1"].fillna(method="ffill")


def process_group(g):
    # Drop first column
    g = g.drop("Unnamed: 1", axis=1)
    # Use first row as column names
    g = g.rename(columns=g.iloc[0])
    # Drop first row
    g = g.drop(g.index[0])
    # Convert to dict
    return g.to_dict(orient="records")


output = df.groupby("Unnamed: 1").apply(process_group).to_dict()

In the end, output is equal to:

{
  "Test 1": [
    {
      "Menu": "Menu1",
      "Setting": "Setting1",
      "Value": "Value1"
    },
    {
      "Menu": "Menu2",
      "Setting": "Setting2",
      "Value": "Value2"
    }
  ],
  "Test 2": [
    {
      "A": 1,
      "B": 2,
      "C": 3
    },
    {
      "A": 4,
      "B": 5,
      "C": 6
    }
  ]
}

You can finally get the JSON string by simply using:

import json

output_str = json.dumps(output)

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