To read a CSV file as a pandas DataFrame, you'll need to use pd.read_csv, which has sep=',' as the default.

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.

When you are storing a DataFrame object into a csv file using the to_csv method, you probably wont be needing to store the preceding indices of each row of the DataFrame object.

You can avoid that by passing a False boolean value to index parameter.

Somewhat like:

df.to_csv(file_name, encoding='utf-8', index=False)

So if your DataFrame object is something like:

  Color  Number
0   red     22
1  blue     10

The csv file will store:

Color,Number
red,22
blue,10

instead of (the case when the default value True was passed)

,Color,Number
0,red,22
1,blue,10