Import CSV file as a pandas DataFrame

You can use the csv module found in the python standard library to manipulate CSV files.

example:

import csv
with open('some.csv', 'rb') as f:
    reader = csv.reader(f)
    for row in reader:
        print row

Note quite as clean, but:

import csv

with open("value.txt", "r") as f:
    csv_reader = reader(f)
    num = '  '
    for row in csv_reader:
        print num, 't'.join(row)
        if num == '  ':  
            num=0
        num=num+1

Not as compact, but it does the job:

   Date price   factor_1    factor_2
1 2012-06-11    1600.20 1.255   1.548
2 2012-06-12    1610.02 1.258   1.554
3 2012-06-13    1618.07 1.249   1.552
4 2012-06-14    1624.40 1.253   1.556
5 2012-06-15    1626.15 1.258   1.552
6 2012-06-16    1626.15 1.263   1.558
7 2012-06-17    1626.15 1.264   1.572

Here’s an alternative to pandas library using Python’s built-in csv module.

import csv
from pprint import pprint
with open('foo.csv', 'rb') as f:
    reader = csv.reader(f)
    headers = reader.next()
    column = {h:[] for h in headers}
    for row in reader:
        for h, v in zip(headers, row):
            column[h].append(v)
    pprint(column)    # Pretty printer

will print

{'Date': ['2012-06-11',
          '2012-06-12',
          '2012-06-13',
          '2012-06-14',
          '2012-06-15',
          '2012-06-16',
          '2012-06-17'],
 'factor_1': ['1.255', '1.258', '1.249', '1.253', '1.258', '1.263', '1.264'],
 'factor_2': ['1.548', '1.554', '1.552', '1.556', '1.552', '1.558', '1.572'],
 'price': ['1600.20',
           '1610.02',
           '1618.07',
           '1624.40',
           '1626.15',
           '1626.15',
           '1626.15']}

To read a CSV file as a pandas DataFrame, you’ll need to use pd.read_csv.

But this isn’t where the story ends; data exists in many different formats and is stored in different ways so you will often need to pass additional parameters to read_csv to ensure your data is read in properly.

Here’s a table listing common scenarios encountered with CSV files along with the appropriate argument you will need to use. You will usually need all or some combination of the arguments below to read in your data.

┌───────────────────────────────────────────────────────┬───────────────────────┬────────────────────────────────────────────────────┐
│ pandas Implementation                                 │ Argument              │ Description                                        │
├───────────────────────────────────────────────────────┼───────────────────────┼────────────────────────────────────────────────────┤
│ pd.read_csv(..., sep=';')                             │ sep/delimiter         │ Read CSV with different separator¹                 │
│ pd.read_csv(..., delim_whitespace=True)               │ delim_whitespace      │ Read CSV with tab/whitespace separator             │
│ pd.read_csv(..., encoding='latin-1')                  │ encoding              │ Fix UnicodeDecodeError while reading²              │
│ pd.read_csv(..., header=False, names=['x', 'y', 'z']) │ header and names      │ Read CSV without headers³                          │
│ pd.read_csv(..., index_col=[0])                       │ index_col             │ Specify which column to set as the index⁴          │
│ pd.read_csv(..., usecols=['x', 'y'])                  │ usecols               │ Read subset of columns                             │
│ pd.read_csv(..., thousands='.', decimal=',')          │ thousands and decimal │ Numeric data is in European format (eg., 1.234,56) │
└───────────────────────────────────────────────────────┴───────────────────────┴────────────────────────────────────────────────────┘

Footnotes

1. By default, read_csv uses a C parser engine for performance. The C parser can only handle single character separators. If your CSV has
   a multi-character separator, you will need to modify your code to use
   the 'python' engine. You can also pass regular expressions:

    df = pd.read_csv(..., sep=r's*|s*', engine='python')
   

2. UnicodeDecodeError occurs when the data was stored in one encoding format but read in a different, incompatible one. Most common
   encoding schemes are 'utf-8' and 'latin-1', your data is likely to
   fit into one of these.

3. header=False specifies that the first row in the CSV is a data row rather than a header row, and the names=[...] allows you to
   specify a list of column names to assign to the DataFrame when it is
   created.

4. "Unnamed: 0" occurs when a DataFrame with an un-named index is saved to CSV and then re-read after. Instead of having to fix the
   issue while reading, you can also fix the issue when writing by using

    df.to_csv(..., index=False)
   

There are other arguments I’ve not mentioned here, but these are the ones you’ll encounter most frequently.

import pandas as pd
df = pd.read_csv('/PathToFile.txt', sep = ',')

This will import your .txt or .csv file into a DataFrame.