Dynamically Creating Pandas DataFrames from Class Objects Found in Different Folders

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In this article, we will explore how to dynamically create pandas dataframes for class objects found in different folders. We’ll use Python’s pandas library and the os module to achieve this.

Understanding the Problem

We are given a set of Excel files that contain information about entities, such as their name, location, and other relevant details. These entities are stored in CSV files located in different folders based on their name and location. Our goal is to create pandas dataframes for each entity found in the list Fruits, which contains class objects representing these entities.

Background Information

Before we dive into the solution, let’s cover some important concepts:

• Excel Files: Excel files are used to store data in a tabular format. We use the pd.ExcelFile function from pandas to read Excel files.
• CSV Files: CSV (Comma Separated Values) files are text files that contain tabular data, similar to Excel files. We use the pd.read_csv function from pandas to read CSV files.
• Class Objects: In Python, a class is a blueprint for creating objects. Class objects have attributes and methods that can be used to manipulate and access data.

Solution

We’ll break down the solution into several steps:

Step 1: Finding All CSV Files in Different Folders

To find all CSV files in different folders, we use the os.walk function, which returns a tuple containing the current directory (root), a list of subdirectories (dirs), and a list of files (files). We then iterate over each file in the files list to check if it’s a CSV file.

import os
import pandas as pd

# Define the root folder
rt = "c:/"

# Create an empty list to store the dataframes
fruits = []

for root, dirs, files in os.walk(rt):
    for fname in files:
        # Check if the file is a CSV file
        if re.match("^.*.csv$", fname):
            # Read the CSV file into a dataframe
            frame = pd.read_csv(os.path.join(root, fname))
            # Append the dataframe to the list of dataframes
            fruits.append(frame)

# Concatenate all dataframes into one large dataframe
df = pd.concat(fruits)

Step 2: Creating Class Objects and Finding Their Respective CSV Files

Next, we create class objects for each entity in the Fruits list. We then find their respective CSV files using the name and location of the entity.

# Define a function to find the csv file for an entity
def pathlocation(Name, Location):
    # Construct the full path to the csv file
    dest_dir = os.path.join("c:", Location)
    fle = os.path.join(dest_dir, Name, "TwoHours.csv")
    return fle

# Create class objects and their respective csv files
for idx, rows in df.iterrows():
    fle = pathlocation(rows["Name"], rows["Location"])
    # Read the CSV file into a dataframe
    col_list = ['Name', 'Date', 'Time', 'Open', 'High', 'Low', 'Close', 'Volume', 'VWAP', 'Trades']
    df3 = pd.read_csv(fle, usecols=col_list, sep=";")
    # Append the dataframe to the list of dataframes
    fruits.append(df3)

# Print the list of dataframes
print("List of Fruits to download data from")
print(fruits)

Step 3: Concatenating All Dataframes into One Large DataFrame

Finally, we concatenate all dataframes in the fruits list into one large dataframe using the pd.concat function.

# Concatenate all dataframes into one large dataframe
fulldf = pd.concat(fruits)

Conclusion

In this article, we explored how to dynamically create pandas dataframes for class objects found in different folders. We used Python’s pandas library and the os module to achieve this.

We demonstrated three steps:

1. Finding all CSV files in different folders using os.walk.
2. Creating class objects and finding their respective CSV files.
3. Concatenating all dataframes into one large dataframe.

This solution can be applied to various scenarios where you need to work with data stored in different folders, such as data analysis, machine learning, or data visualization projects.

The complete code for this article is shown below:

import os
import pandas as pd

# Define the root folder
rt = "c:/"

# Create an empty list to store the dataframes
fruits = []

for root, dirs, files in os.walk(rt):
    for fname in files:
        # Check if the file is a CSV file
        if re.match("^.*.csv$", fname):
            # Read the CSV file into a dataframe
            frame = pd.read_csv(os.path.join(root, fname))
            # Append the dataframe to the list of dataframes
            fruits.append(frame)

# Concatenate all dataframes into one large dataframe
df = pd.concat(fruits)

def pathlocation(Name, Location):
    dest_dir = os.path.join("c:", Location)
    fle = os.path.join(dest_dir, Name, "TwoHours.csv")
    return fle

for idx, rows in df.iterrows():
    fle = pathlocation(rows["Name"], rows["Location"])
    col_list = ['Name', 'Date', 'Time', 'Open', 'High', 'Low', 'Close', 'Volume', 'VWAP', 'Trades']
    df3 = pd.read_csv(fle, usecols=col_list, sep=";")
    fruits.append(df3)

print("List of Fruits to download data from")
print(fruits)

fulldf = pd.concat(fruits)

Please note that this is just an example and may need modifications based on your specific requirements. Also, the actual file paths (rt) should be replaced with the desired root folder.

I hope this helps you in solving the problem of dynamically creating pandas dataframes for class objects found in different folders!

Last modified on 2024-03-31