Converting Multiple Dictionary Rows to a Pandas DataFrame

In this article, we’ll learn how to convert multiple dictionary rows from a CSV file to a Pandas DataFrame. This is especially useful when you have a script that processes commands from multiple rows in the CSV file and needs to be integrated with other parts of your system.

Let’s say you have a script that reads command parameters from a CSV file using a “for” loop:

data = pd.read_csv('command parameters.csv')
df = pd.DataFrame(data)

However, each row in the CSV file represents an individual command parameter. If your data contains multiple dictionary rows, you’ll need to convert them to a single DataFrame.

To achieve this, we can use the “apply” method and apply it to each row of the dictionary in turn. Here is an example:

import pandas as pd

Define a function that converts a single dictionary row to a Pandas Series (i.e. a column)
def dict_to_series(row):
return pd.Series([dict[row].get('value', None)], index=['key'])

Apply this function to each row of the DataFrame
df['orderparameter'] = df.apply(dict_to_series, axis=1)
print(df)

This will create a new column called “orderparameter” in your original DataFrame. The value of each cell is a Pandas Series containing a value from each row of the dictionary.

Here’s what happens when we call the dict_to_series function on each row of the DataFrame:

• df.apply(dict_to_series, axis=1) applies this function to each row (index 0) in turn. The axis=1 parameter tells Pandas that we want to apply the function element-wise along the rows.

• axis=1 means that we treat each row of the dictionary as an individual column in our DataFrame.

The result is a new Series column called “orderparameter”, where each cell contains a value from the corresponding dictionary row. If there are missing values ​​(i.e. no “value” key in the dictionary), they will be represented as “None”.

Suppose your CSV file has two rows:

{'id': 1, 'quantity': 10, 'price': 100},
{'id': 2, 'quantity': 20, 'price': 200}

When you run the script that reads these rows into a DataFrame, it creates a new column called “orderparameter” with values ​​like this:

| main | quantity | price | orderparameter |
| --- | --- | --- | --- |
| 1 | 10 | 100 | [10.0] |
| 2 | 20 | 200 | [20.0] |

Note that the values ​​in each cell are lists, as expected.

By following this approach, you can easily convert multiple rows of dictionaries into a Pandas DataFrame ready for further processing or analysis.

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