Read data from CSV file and transform from string to correct data-type, including a list-of-integer column

Question

When I read data back in from a CSV file, every cell is interpreted as a string.

• How can I automatically convert the data I read in into the correct type?

Answer 1

An alternative (although it seems a bit extreme) in lieu of using ast.literal_eval is the pyparsing module available on PyPi - and see if the http://pyparsing.wikispaces.com/file/view/parsePythonValue.py code sample is either appropriate for what you require, or can be easily adapted.

Answer 2

You have to map your rows:

data = """True,foo,1,2.3,baz
False,bar,7,9.8,qux"""

reader = csv.reader(StringIO.StringIO(data), delimiter=",")
parsed = (({'True':True}.get(row[0], False),
           row[1],
           int(row[2]),
           float(row[3]),
           row[4])
          for row in reader)
for row in parsed:
    print row

results in

(True, 'foo', 1, 2.3, 'baz')
(False, 'bar', 7, 9.8, 'qux')

Answer 3

Props to Jon Clements and cortopy for teaching me about ast.literal_eval! Here's what I ended up going with (Python 2; changes for 3 should be trivial):

from ast import literal_eval
from csv import DictReader
import csv


def csv_data(filepath, **col_conversions):
    """Yield rows from the CSV file as dicts, with column headers as the keys.

    Values in the CSV rows are converted to Python values when possible,
    and are kept as strings otherwise.

    Specific conversion functions for columns may be specified via
    `col_conversions`: if a column's header is a key in this dict, its
    value will be applied as a function to the CSV data. Specify
    `ColumnHeader=str` if all values in the column should be interpreted
    as unquoted strings, but might be valid Python literals (`True`,
    `None`, `1`, etc.).

    Example usage:

    >>> csv_data(filepath,
    ...          VariousWordsIncludingTrueAndFalse=str,
    ...          NumbersOfVaryingPrecision=float,
    ...          FloatsThatShouldBeRounded=round,
    ...          **{'Column Header With Spaces': arbitrary_function})
    """

    def parse_value(key, value):
        if key in col_conversions:
            return col_conversions[key](value)
        try:
            # Interpret the string as a Python literal
            return literal_eval(value)
        except Exception:
            # If that doesn't work, assume it's an unquoted string
            return value

    with open(filepath) as f:
        # QUOTE_NONE: don't process quote characters, to avoid the value
        # `"2"` becoming the int `2`, rather than the string `'2'`.
        for row in DictReader(f, quoting=csv.QUOTE_NONE):
            yield {k: parse_value(k, v) for k, v in row.iteritems()}

(I'm a little wary that I might have missed some corner cases involving quoting. Please comment if you see any issues!)

Answer 4

I know this is a fairly old question, tagged python-2.5, but here's answer that works with Python 3.6+ which might be of interest to folks using more up-to-date versions of the language.

It leverages the built-in typing.NamedTuple class which was added in Python 3.5. What may not be evident from the documentation is that the "type" of each field can be a function.

The example usage code also uses so-called f-string literals which weren't added until Python 3.6, but their use isn't required to do the core data-type transformations.

#!/usr/bin/env python3.6
import ast
import csv
from typing import NamedTuple


class Record(NamedTuple):
    """ Define the fields and their types in a record. """
    IsActive: bool
    Type: str
    Price: float
    States: ast.literal_eval  # Handles string represenation of literals.

    @classmethod
    def _transform(cls: 'Record', dct: dict) -> dict:
        """ Convert string values in given dictionary to corresponding Record
            field type.
        """
        return {name: cls.__annotations__[name](value)
                    for name, value in dict_.items()}


filename = 'test_transform.csv'

with open(filename, newline='') as file:
    for i, row in enumerate(csv.DictReader(file)):
        row = Record._transform(row)
        print(f'row {i}: {row}')

Output:

row 0: {'IsActive': True, 'Type': 'Cellphone', 'Price': 34.0, 'States': [1, 2]}
row 1: {'IsActive': False, 'Type': 'FlatTv', 'Price': 3.5, 'States': [2]}
row 2: {'IsActive': True, 'Type': 'Screen', 'Price': 100.23, 'States': [5, 1]}
row 3: {'IsActive': True, 'Type': 'Notebook', 'Price': 50.0, 'States': [1]}

Generalizing this by creating a base class with just the generic classmethod in it is not simple because of the way typing.NamedTuple is implemented.

To avoid that issue, in Python 3.7+, a dataclasses.dataclass could be used instead because they do not have the inheritance issue — so creating a generic base class that can be reused is simple:

#!/usr/bin/env python3.7
import ast
import csv
from dataclasses import dataclass, fields
from typing import Type, TypeVar

T = TypeVar('T', bound='GenericRecord')

class GenericRecord:
    """ Generic base class for transforming dataclasses. """
    @classmethod
    def _transform(cls: Type[T], dict_: dict) -> dict:
        """ Convert string values in given dictionary to corresponding type. """
        return {field.name: field.type(dict_[field.name])
                    for field in fields(cls)}


@dataclass
class CSV_Record(GenericRecord):
    """ Define the fields and their types in a record.
        Field names must match column names in CSV file header.
    """
    IsActive: bool
    Type: str
    Price: float
    States: ast.literal_eval  # Handles string represenation of literals.


filename = 'test_transform.csv'

with open(filename, newline='') as file:
    for i, row in enumerate(csv.DictReader(file)):
        row = CSV_Record._transform(row)
        print(f'row {i}: {row}')

In one sense it's not really very important which one you use because an instance of the class in never created — using one is just a clean way of specifying and holding a definition of the field names and their type in a record data-structure.

A TypeDict was added to the typing module in Python 3.8 that can also be used to provide the typing information, but must be used in a slightly different manner since it doesn't actually define a new type like NamedTuple and dataclasses do — so it requires having a standalone transforming function:

#!/usr/bin/env python3.8
import ast
import csv
from dataclasses import dataclass, fields
from typing import TypedDict


def transform(dict_, typed_dict) -> dict:
    """ Convert values in given dictionary to corresponding types in TypedDict . """
    fields = typed_dict.__annotations__
    return {name: fields[name](value) for name, value in dict_.items()}


class CSV_Record_Types(TypedDict):
    """ Define the fields and their types in a record.
        Field names must match column names in CSV file header.
    """
    IsActive: bool
    Type: str
    Price: float
    States: ast.literal_eval


filename = 'test_transform.csv'

with open(filename, newline='') as file:
    for i, row in enumerate(csv.DictReader(file), 1):
        row = transform(row, CSV_Record_Types)
        print(f'row {i}: {row}')

Answer 5

I too really liked @martineau's approach and was especially intrigued by his comment that the essence of his code was a clean mapping between fields and types. That suggested to me that a dictionary would work also. Hence the variation on his theme shown below. It's worked nicely for me.

Clearly the value field in the dictionary is really just a callable and thus could be used to provide a hook for data massaging as well as typecasting if one so chose.

import ast
import csv

fix_type = {'IsActive': bool, 'Type': str, 'Price': float, 'States': ast.literal_eval}

filename = 'test_transform.csv'

with open(filename, newline='') as file:
    for i, row in enumerate(csv.DictReader(file)):
        row = {k: fix_type[k](v) for k, v in row.items()}
        print(f'row {i}: {row}')

Output

row 0: {'IsActive': True, 'Type': 'Cellphone', 'Price': 34.0, 'States': [1, 2]}
row 1: {'IsActive': False, 'Type': 'FlatTv', 'Price': 3.5, 'States': [2]}
row 2: {'IsActive': True, 'Type': 'Screen', 'Price': 100.23, 'States': [5, 1]}
row 3: {'IsActive': True, 'Type': 'Notebook', 'Price': 50.0, 'States': [1]}

Answer 6

As the docs explain, the CSV reader doesn't perform automatic data conversion. You have the QUOTE_NONNUMERIC format option, but that would only convert all non-quoted fields into floats. This is a very similar behaviour to other csv readers.

I don't believe Python's csv module would be of any help for this case at all. As others have already pointed out, literal_eval() is a far better choice.

The following does work and converts:

• strings
• int
• floats
• lists
• dictionaries

You may also use it for booleans and NoneType, although these have to be formatted accordingly for literal_eval() to pass. LibreOffice Calc displays booleans in capital letters, when in Python booleans are Capitalized. Also, you would have to replace empty strings with None (without quotes)

I'm writing an importer for mongodb that does all this. The following is part of the code I've written so far.

[NOTE: My csv uses tab as field delimiter. You may want to add some exception handling too]

def getFieldnames(csvFile):
    """
    Read the first row and store values in a tuple
    """
    with open(csvFile) as csvfile:
        firstRow = csvfile.readlines(1)
        fieldnames = tuple(firstRow[0].strip('\n').split("\t"))
    return fieldnames

def writeCursor(csvFile, fieldnames):
    """
    Convert csv rows into an array of dictionaries
    All data types are automatically checked and converted
    """
    cursor = []  # Placeholder for the dictionaries/documents
    with open(csvFile) as csvFile:
        for row in islice(csvFile, 1, None):
            values = list(row.strip('\n').split("\t"))
            for i, value in enumerate(values):
                nValue = ast.literal_eval(value)
                values[i] = nValue
            cursor.append(dict(zip(fieldnames, values)))
    return cursor