Load data from txt with pandas

Dealing with space-separated files

For space-separated files, make space the separator using " ":

df = pd.read_csv('data.txt', sep=' ')  # Space, the final frontier
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When there's no headers present, use header=None. This prevents Pandas from considering the first data row as the header:

df = pd.read_csv('data.txt', sep=' ', header=None)  # Playing "Hide and Seek" with headers
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After the file is loaded, assign column names to enhance the usability of your data:

df.columns = ['Col1', 'Col2', 'Col3']  # Giving your columns the cool names they deserve
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When you're dealing with fixed-width formatted files or inconsistent spacing, use pd.read_fwf(). This is Pandas' way of saying, "I've got this, just let me handle it":

df = pd.read_fwf('data.txt')  # Pandas, dealing with awkward spaces so you don't have to
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Ensuring data is read correctly

It's not just about reading data; it's about reading it right. You may need to deal with complex delimiters or specify column names upfront:

df = pd.read_csv('data.txt', delimiter='[,\t;]+', engine='python')  # Multi-delimiter chaos, unleashed
df = pd.read_csv('data.txt', sep=' ', names=['ID', 'Val1', 'Val2'], header=None)  # Call me by my name
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Taking care of potential issues

Incorrect data parse

If your data appears incorrect, check your file path and delimiter. A mere mismatch can bungle up your DataFrame:

import os
if os.path.exists('data.txt'):
    df = pd.read_csv('data.txt', sep='|')  # "|" or not "|", that is the question
else:
    print("File not found.")  # File playing "Hide and Seek"
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Data type mismatch

Pandas automatically infers data types, but can get confused by mixed data types. Specify dtype upfront:

df = pd.read_csv('data.txt', sep=' ', dtype={'ID': int, 'Val1': float})  # Pandas, you classification junkie!
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Memory constraints

When processing large files, data size can be an issue. Using chunksize or iterator parameters can come in handy:

chunk_iter = pd.read_csv('data.txt', chunksize=10000)  # Pandas diet: small chunks, less memory
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Data Manipulation Simplified

Once you've tackled the data import, all you need is ways to access and manipulate your data effectively:

# Accessing data at the second row, third column
value = df.iloc[1, 2]  # Playing "Hide and Seek" with data elements

# Creating a new dataframe based on specific conditions
new_df = df[df['Col1'] > some_value]  # Your very own slice of data

# Transforming data with a lambda function
df['Col2'] = df['Col2'].apply(lambda x: x * 2)  # Because who doesn't like a little change?
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