dataiter

The following functions are shorthand helpers for use in conjunction with DataFrame.aggregate(), see the guide on aggregation for details.

all() any() count() count_unique() first() last() max() mean() median() min() mode() nth() quantile() std() sum() var()

The following read functions are convenience aliases to the correspoding methods of the classes generally most suitable for the particular file type, i.e. DataFrame for CSV, NPZ and Parquet, GeoJSON for GeoJSON and ListOfDicts for JSON.

read_csv() read_geojson() read_json() read_npz() read_parquet()

The following constants can be used to customize certain defaults, such as formatting and limits for printing.

dataiter.PRINT_MAX_WIDTH dataiter.PRINT_THOUSAND_SEPARATOR dataiter.PRINT_TRUNCATE_WIDTH dataiter.USE_NUMBA dataiter.USE_NUMBA_CACHE

dataiter.PRINT_MAX_WIDTH = 80

Maximum amount of columns to wrap print output to. This is only a fallback in case Python’s shutil.get_terminal_size fails to detect the width of your terminal. By default the detected full width is used.

dataiter.PRINT_THOUSAND_SEPARATOR = ''

Thousand separator to use when printing numbers. By default this is blank, meaning no thousand separators are rendered.

dataiter.PRINT_TRUNCATE_WIDTH = 36

Maximum width to truncate string columns to in DataFrame print output. When this is exceeded, strings will be cut and an ellipsis (…) rendered at the cut point.

dataiter.USE_NUMBA = False

True to use Numba, if available, to speed up aggregations, False to only use pure Python code.

dataiter.USE_NUMBA_CACHE = True

True to use Numba cache for JIT-compiled aggregations, False to only keep compiled code in memory for the duration of the session.

dataiter.all(x)

Return whether all elements of x evaluate to True.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

Uses numpy.all, see the NumPy documentation for details: https://numpy.org/doc/stable/reference/generated/numpy.all.html

>>> di.all(di.Vector([True, False]))
False
>>> di.all(di.Vector([True, True]))
True
>>> di.all("x")
<function all.<locals>.aggregate at 0x7b314f85b270>

dataiter.any(x)

Return whether any element of x evaluates to True.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

Uses numpy.any, see the NumPy documentation for details: https://numpy.org/doc/stable/reference/generated/numpy.any.html

>>> di.any(di.Vector([False, False]))
False
>>> di.any(di.Vector([True, False]))
True
>>> di.any("x")
<function any.<locals>.aggregate at 0x75e44d0eb270>

dataiter.count(x='', *, drop_na=False)

Return the amount of elements in x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x. Since all columns in a data frame should have the same amount of elements (i.e. rows), you can just leave the x argument at its default blank string, which will give you that row count.

>>> di.count(di.Vector([1, 2, 3]))
3
>>> di.count()
<function count.<locals>.aggregate at 0x79ee69c2b270>

dataiter.count_unique(x, *, drop_na=False)

Return the amount of unique elements in x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

>>> di.count_unique(di.Vector([1, 2, 2, 3, 3, 3]))
3
>>> di.count_unique("x")
<function count_unique.<locals>.aggregate at 0x7b7a064b3270>

dataiter.first(x, *, drop_na=False)

Return the first element of x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

>>> di.first(di.Vector([1, 2, 3]))
1
>>> di.first("x")
<function nth.<locals>.aggregate at 0x7ce5a8bc3270>

dataiter.last(x, *, drop_na=False)

Return the last element of x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

>>> di.last(di.Vector([1, 2, 3]))
3
>>> di.last("x")
<function nth.<locals>.aggregate at 0x7cc42b4ef270>

dataiter.max(x, *, drop_na=True)

Return the maximum of elements in x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

>>> di.max(di.Vector([4, 5, 6]))
6
>>> di.max("x")
<function max.<locals>.aggregate at 0x7e3e695fb270>

dataiter.mean(x, *, drop_na=True)

Return the arithmetic mean of x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

Uses numpy.mean, see the NumPy documentation for details: https://numpy.org/doc/stable/reference/generated/numpy.mean.html

>>> di.mean(di.Vector([1, 2, 10]))
4.333333333333333
>>> di.mean("x")
<function mean.<locals>.aggregate at 0x77f98af3f270>

dataiter.median(x, *, drop_na=True)

Return the median of x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

Uses numpy.median, see the NumPy documentation for details: https://numpy.org/doc/stable/reference/generated/numpy.median.html

>>> di.median(di.Vector([5, 1, 2]))
2.0
>>> di.median("x")
<function median.<locals>.aggregate at 0x725bd5c1f270>

dataiter.min(x, *, drop_na=True)

Return the minimum of elements in x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

>>> di.min(di.Vector([4, 5, 6]))
4
>>> di.min("x")
<function min.<locals>.aggregate at 0x72096b2ab270>

dataiter.mode(x, *, drop_na=True)

Return the most common value in x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

>>> di.mode(di.Vector([1, 2, 2, 3, 3, 3]))
3
>>> di.mode("x")
<function mode.<locals>.aggregate at 0x7a7f4abab270>

dataiter.nth(x, index, *, drop_na=False)

Return the element of x at index (zero-based).

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

>>> di.nth(di.Vector([1, 2, 3]), 1)
2
>>> di.nth("x", 1)
<function nth.<locals>.aggregate at 0x75cb48503110>

dataiter.quantile(x, q, *, drop_na=True)

Return the qth quantile of x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

Uses numpy.quantile, see the NumPy documentation for details: https://numpy.org/doc/stable/reference/generated/numpy.quantile.html

>>> di.quantile(di.Vector([1, 5, 6]), 0.5)
5.0
>>> di.quantile("x", 0.5)
<function quantile.<locals>.aggregate at 0x70be70c47110>

dataiter.read_csv(path, *, encoding='utf-8', sep=',', header=True, columns=[], dtypes={})

Return a new data frame from CSV file path.

Will automatically decompress if path ends in .bz2|.gz|.xz. columns is an optional list of columns to limit to. dtypes is an optional dict mapping column names to NumPy datatypes.

Note

read_csv() is a convenience alias for DataFrame.read_csv().

dataiter.read_geojson(path, *, encoding='utf-8', columns=[], dtypes={}, **kwargs)

Return data from GeoJSON file path.

Will automatically decompress if path ends in .bz2|.gz|.xz. columns is an optional list of columns to limit to. dtypes is an optional dict mapping column names to NumPy datatypes. kwargs are passed to json.load.

Note

read_geojson() is a convenience alias for GeoJSON.read().

dataiter.read_json(path, *, encoding='utf-8', keys=[], types={}, **kwargs)

Return a new list from JSON file path.

Will automatically decompress if path ends in .bz2|.gz|.xz. keys is an optional list of keys to limit to. types is an optional dict mapping keys to datatypes. kwargs are passed to json.load.

Note

read_json() is a convenience alias for ListOfDicts.read_json().

dataiter.read_npz(path, *, allow_pickle=True)

Return a new data frame from NumPy file path.

See numpy.load for an explanation of allow_pickle: https://numpy.org/doc/stable/reference/generated/numpy.load.html

Note

read_npz() is a convenience alias for DataFrame.read_npz().

dataiter.read_parquet(path, *, columns=[], dtypes={})

Return a new data frame from Parquet file path.

columns is an optional list of columns to limit to. dtypes is an optional dict mapping column names to NumPy datatypes.

Note

read_parquet() is a convenience alias for DataFrame.read_parquet().

dataiter.std(x, *, ddof=0, drop_na=True)

Return the standard deviation of x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

Uses numpy.std, see the NumPy documentation for details: https://numpy.org/doc/stable/reference/generated/numpy.std.html

>>> di.std(di.Vector([3, 6, 7]))
1.699673171197595
>>> di.std("x")
<function std.<locals>.aggregate at 0x7dd670f0b270>

dataiter.sum(x, *, drop_na=True)

Return the sum of x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

>>> di.sum(di.Vector([1, 2, 3]))
6
>>> di.sum("x")
<function sum.<locals>.aggregate at 0x79157bac3270>

dataiter.var(x, *, ddof=0, drop_na=True)

Return the variance of x.

If x is a string, return a function usable with DataFrame.aggregate() that operates group-wise on column x.

Uses numpy.var, see the NumPy documentation for details: https://numpy.org/doc/stable/reference/generated/numpy.var.html

>>> di.var(di.Vector([3, 6, 7]))
2.888888888888889
>>> di.var("x")
<function var.<locals>.aggregate at 0x75dbfe353270>