Oppiskele Importing and Cleaning Financial Data | Automating Accounting Workflows

Importing and cleaning financial data are fundamental steps in automating accounting workflows. As an accountant, you often work with raw data exported from various systems, such as bank statements, expense reports, or sales ledgers. These files, frequently in CSV format, may contain inconsistencies like missing values, incorrect data types, or duplicate entries. If left unaddressed, such issues can lead to inaccurate analyses or flawed reports. Ensuring your data is clean and properly formatted before analysis not only saves time but also increases the reliability of your financial insights.

import pandas as pd

# Read a CSV file containing transaction data
df = pd.read_csv("example.csv")

# Display the first five rows
print(df.head())

A pandas DataFrame is a two-dimensional, tabular data structure with labeled axes (rows and columns), similar to a spreadsheet. Each column can hold data of different types, such as numbers, dates, or text. When working with financial datasets, you often need to clean the data to ensure accuracy. Common data cleaning steps include:

Handling missing values by filling or removing them;
Correcting data types (for instance, ensuring that an Amount column is numeric);
Eliminating duplicate rows.

These steps are crucial because errors or inconsistencies in your data can lead to miscalculations, misreporting, or compliance issues in your accounting tasks.


              123456789101112131415161718192021
            
import pandas as pd

# Create a sample DataFrame with some missing values and duplicates
data = {
    "Date": ["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04", "2024-01-05", "2024-01-01"],
    "Description": ["Deposit", "Payment", "Subscription", "Deposit", "Payment", "Deposit"],
    "Amount": [250.0, -75.0, None, 200.0, -50.0, 250.0]
}
df = pd.DataFrame(data)

# Fill missing values in the 'Amount' column with 0
df["Amount"] = df["Amount"].fillna(0)

# Convert the 'Amount' column to float type
df["Amount"] = df["Amount"].astype(float)

# Remove duplicate rows
df = df.drop_duplicates()

# Display the cleaned DataFrame
print(df.head())

1. Which pandas function is commonly used to read CSV files into a DataFrame?

2. Why is it important to handle missing values in financial datasets?

3. Fill in the blanks to clean a DataFrame by removing duplicates and converting a column to float.

Oliko kaikki selvää?

Kiitos palautteestasi!

Osio 1. Luku 1

Kysy tekoälyä

Kysy mitä tahansa tai kokeile jotakin ehdotetuista kysymyksistä aloittaaksesi keskustelumme

Suggested prompts:

Can you explain how to handle missing values differently, like using the mean or median?

What should I do if my data has incorrect date formats?

How can I identify and remove only specific types of duplicates?

Pyyhkäise näyttääksesi valikon

import pandas as pd

# Read a CSV file containing transaction data
df = pd.read_csv("example.csv")

# Display the first five rows
print(df.head())

Handling missing values by filling or removing them;
Correcting data types (for instance, ensuring that an Amount column is numeric);
Eliminating duplicate rows.