Python import csv to list

Compatibility: Python 2.x vs 3.x

Python has evolved and so has the way it handles CSVs. With Python 3, you should use newline='' when opening files, ensuring the correct interpretation of newline characters:

# Python 3.x
with open('filename.csv', newline='') as file:
    data_list = [row for row in csv.reader(file)]
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For Python 2.x, use binary mode ('rb') when you want to read the CSV data correctly:

# Python 2.x
with open('filename.csv', 'rb') as file:
    data_list = [row for row in csv.reader(file)]
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Remember, coding is like making coffee: brewing methods may vary but the goal is always the same. ☕

Parsing complex fields

Your CSV data might resemble a raccoon's dumpster feast - adding commas everywhere! Fear not, csv.reader eats complex data for breakfast. Thanks to its delimiter and quotechar parameters, it ensures parsing is handled with finesse:

with open('filename.csv', newline='') as file:
    data_list = [row for row in csv.reader(file, delimiter=',', quotechar='"')]
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Power up with pandas

Pandas is a Swiss Army knife for data wrangling. When dealing with large datasets, manual processing is as appealing as chewing cardboard. With pd.read_csv(), Pandas handles CSV data like a champ:

import pandas as pd

df = pd.read_csv('filename.csv')  # DataFrame to the rescue!
data_list = df.values.tolist()    # Converts DataFrame to a list of lists
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And for a list of tuples:

data_tuples = list(df.itertuples(index=False, name=None))  # Tuple power!
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Sometimes you might need a list of dictionaries. It’s like eating your favorite pizza piece by piece:

dict_list = df.to_dict(orient='records')  # Now serving dictionaries! 
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Data plot twist with seaborn

Seaborn is your painting kit to plot data epic stories. It's like describing your favorite movie plots with shapes and colors. Together with pandas, it provides sophisticated data visualization tools:

import seaborn as sns
import matplotlib.pyplot as plt

# Load the dataset into a pandas DataFrame
df = pd.read_csv('filename.csv')  

# Let Seaborn steal the show
sns.barplot(x='category', y='value', data=df)
plt.show()    # The crowd goes wild!
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Seaborn has pages and pages of plot diagrams, think of it as a comic book store for your data.

Custom parsing for intricate CSVs

Some CSV files are like a messy teenager's room. To clean up, you sometimes need to do it yourself. When built-in csv reader fails to combat the mess, you need to whip out your secret parsing weapon:

with open('filename.csv', 'r') as file:
    data_list = []
    for line in file:
        # Dusting off custom parsing skills
        parsed_line = my_complex_parser(line)
        data_list.append(parsed_line)
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Welcome to the Order of Custom Parsers! Your badge is in the mail. 🏅

Continual honing of skills

Any adventure begins with the first step and programming is no different. The quest for better code is made easier by the many resources available to aid you in battle.