API documentation

Knead

class dataknead.Knead(inp, parse_as=None, read_as=None, is_data=False, **kwargs)

__init__(inp, parse_as=None, read_as=None, is_data=False, **kwargs)

Creates a new Knead instance.

By default it is assumed that inp is a path to a file if it’s a string. The file extension will be used to determine the correct loader (e.g. .csv will get the CsvLoader).

Knead("cities.csv")

If inp is not a string it is assumed to be a data structure (e.g. a list or dict).

Knead([1, 2, 3])

parse_as is used when you’re feeding Knead a string that is not a file path, like the results of a HTTP call. In that case you need to specify the loader yourself as a file extension (e.g. csv, json, etc.)

json = "[1,2,3]"
Knead(json, parse_as = "json")

read_as is used when you’re giving a path to a file that doesn’t have a valid extension, and doesn’t have an extension at all. The same mechanism as parse_as is at work here: give it a file extension.

Knead("query", read_as = "csv")

is_data is used if you want to feed Knead a string as data, and it shouldn’t be parsed at all

Knead("a line of text", is_data = True)

Any additional parameters will be passed on to the loader

Knead("cities.csv", has_header = True)

add_loader(loader)

Adds a custom loader. See extending dataknead.

apply(fn)

This applies a function to the loaded data.

data(check_instance=None)

Returns the loaded data.

Knead("cities.csv").data() # Will return the parsed contents of cities.csv

To check if the data is in a specific type use check_instance, which will raise an exception if it’s not correct

Knead([1,2,3]).data(check_instance = dict) # Will raise an error, it's a list

filter(fn)

Applies a function on every item and removes it if it returns False. This only works when your data is a list.

Knead([1,10,100]).filter(lambda v: v > 9).data() # Returns 10 and 100

keys()

Returns the keys of the internal data

loader

alias of dataknead.loaders.xml.XmlLoader

map(iteratee)

Applies a function on every item and replaces it with the return value.

Knead([1, 4, 9]).map(lambda x:x * x).data() # [1, 16, 81]

If you have a list with dicts, you can give a string to return the values of a specific key

Knead("cities.csv").map("city").write("city-names.csv")

# Is the same as

Knead("cities.csv").map(lambda c:c["city"]).write("city-names.csv")

To return multiple keys with values, you can use a tuple:

Knead("cities.csv").map(("city", "country")).write("city-country-names.csv")

# Is the same as

Knead("cities.csv").map(lambda c:{ "city" : c["city"], "country" : c["country"] }).write("city-country-names.csv")

# Or

def mapcity(city):
    return {
        "city" : city["city"],
        "country" : city["country"]
    }

Knead("cities.csv").map(mapcity).write("city-country-names.csv")

values()

Returns the values of the data

write(path, write_as=None, **kwargs)

Writes the data to a file. Type is implied by file extension.

Knead("cities.csv").write("cities.json")

To force the type to something else, pass the format to write_as.

Knead("cities.csv").map("city").write("cities.txt", write_as="csv")

Any additional parameters will be passed on to the loader:

Knead("cities.csv").write("cities.json", indent = 4)
Knead("cities.csv").map("city").write("ciites.csv", fieldnames=["city"])

Loaders

These loaders are available by default. Note that you never need to access these directly, they are merely documented here for reference purposes and as an example if you want to write your own loader.

CsvLoader

class dataknead.loaders.csv.CsvLoader

ExcelLoader

class dataknead.loaders.excel.ExcelLoader

JsonLoader

class dataknead.loaders.json.JsonLoader

TextLoader

class dataknead.loaders.text.TextLoader

TomlLoader

class dataknead.loaders.toml.TomlLoader

XmlLoader

class dataknead.loaders.xml.XmlLoader