Bundled datasets¶

These objects are loaded at import time from CSV files bundled with the package.

eurocontrolpy.member_state = name iso3c iso2c icao iso3n date status 0 Albania ALB AL LA 8 1991-11-05 M 1 Armenia ARM AM UD 51 2012-01-01 M 2 Austria AUT AT LO 40 1993-01-01 M 3 Belgium BEL BE EB 56 1960-05-06 M 4 Bosnia and Herzegovina BIH BA LQ 70 2001-12-01 M 5 Bulgaria BGR BG LB 100 1993-01-01 M 6 Croatia HRV HR LD 191 1997-04-01 M 7 Cyprus CYP CY LC 196 2003-03-01 M 8 Czech Republic CZE CZ LK 203 1993-01-01 M 9 Denmark DNK DK EK 208 1960-05-06 M 10 Estonia EST EE EE 233 1996-11-13 M 11 Finland FIN FI EF 246 1960-05-06 M 12 France FRA FR LF 250 1960-05-06 M 13 Georgia GEO GE UG 268 2010-07-01 M 14 Germany DEU DE ED 276 1960-05-06 M 15 Greece GRC GR LG 300 1960-05-06 M 16 Hungary HUN HU LH 348 1993-01-01 M 17 Ireland IRL IE EI 372 1960-05-06 M 18 Italy ITA IT LI 380 1960-05-06 M 19 Kosovo XKX XK NaN 0 NaT NaN 20 Latvia LVA LV EV 428 1997-11-12 M 21 Lithuania LTU LT EY 440 1997-11-07 M 22 Luxembourg LUX LU EL 442 1960-05-06 M 23 Malta MLT MT LM 470 1998-01-07 M 24 Moldova MDA MD LU 498 2007-01-24 M 25 Monaco MCO MC LN 492 2005-09-28 M 26 Montenegro MNE ME LY 499 2011-01-01 M 27 Morocco MAR MA GM 504 2006-09-06 C 28 Netherlands NLD NL EH 528 1960-05-06 M 29 North Macedonia MKD MK LW 807 2002-02-27 M 30 Norway NOR NO EN 578 1960-05-06 M 31 Poland POL PL EP 616 1994-04-06 M 32 Portugal PRT PT LP 620 1960-05-06 M 33 Romania ROU RO LR 642 1993-01-01 M 34 Serbia SRB RS LY 688 2011-01-01 M 35 Slovak Republic SVK SK LZ 703 1993-01-01 M 36 Slovenia SVN SI LJ 705 1994-04-01 M 37 Spain ESP ES LE 724 1960-05-06 M 38 Sweden SWE SE ES 752 1960-05-06 M 39 Switzerland CHE CH LS 756 1960-05-06 M 40 Tunisia TUN TN DT 788 2012-11-01 C 41 Turkey TUR TR LT 792 1960-05-06 M 42 Ukraine UKR UA UK 804 2004-01-14 M 43 United Kingdom GBR GB EG 826 1960-05-06 M¶

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.

Parameters:

data (ndarray (structured or homogeneous), Iterable, dict, or DataFrame) –
Dict can contain Series, arrays, constants, dataclass or list-like objects. If data is a dict, column order follows insertion-order. If a dict contains Series which have an index defined, it is aligned by its index. This alignment also occurs if data is a Series or a DataFrame itself. Alignment is done on Series/DataFrame inputs.

If data is a list of dicts, column order follows insertion-order.
index (Index or array-like) – Index to use for resulting frame. Will default to RangeIndex if no indexing information part of input data and no index provided.
columns (Index or array-like) – Column labels to use for resulting frame when data does not have them, defaulting to RangeIndex(0, 1, 2, …, n). If data contains column labels, will perform column selection instead.
dtype (dtype, default None) – Data type to force. Only a single dtype is allowed. If None, infer. If data is DataFrame then is ignored.
copy (bool or None, default None) – Copy data from inputs. For dict data, the default of None behaves like copy=True. For DataFrame or 2d ndarray input, the default of None behaves like copy=False. If data is a dict containing one or more Series (possibly of different dtypes), copy=False will ensure that these inputs are not copied.

See also

DataFrame.from_records: Constructor from tuples, also record arrays.
DataFrame.from_dict: From dicts of Series, arrays, or dicts.
read_csv: Read a comma-separated values (csv) file into DataFrame.
read_table: Read general delimited file into DataFrame.
read_clipboard: Read text from clipboard into DataFrame.

Notes

Please reference the User Guide for more information.

Examples

Constructing DataFrame from a dictionary.

>>> d = {"col1": [1, 2], "col2": [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
   col1  col2
0     1     3
1     2     4

Notice that the inferred dtype is int64.

>>> df.dtypes
col1    int64
col2    int64
dtype: object

To enforce a single dtype:

>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1    int8
col2    int8
dtype: object

Constructing DataFrame from a dictionary including Series:

>>> d = {"col1": [0, 1, 2, 3], "col2": pd.Series([2, 3], index=[2, 3])}
>>> pd.DataFrame(data=d, index=[0, 1, 2, 3])
   col1  col2
0     0   NaN
1     1   NaN
2     2   2.0
3     3   3.0

Constructing DataFrame from numpy ndarray:

>>> df2 = pd.DataFrame(
...     np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=["a", "b", "c"]
... )
>>> df2
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

Constructing DataFrame from a numpy ndarray that has labeled columns:

>>> data = np.array(
...     [(1, 2, 3), (4, 5, 6), (7, 8, 9)],
...     dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")],
... )
>>> df3 = pd.DataFrame(data, columns=["c", "a"])
>>> df3
   c  a
0  3  1
1  6  4
2  9  7

Constructing DataFrame from dataclass:

>>> from dataclasses import make_dataclass
>>> Point = make_dataclass("Point", [("x", int), ("y", int)])
>>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
   x  y
0  0  0
1  0  3
2  2  3

Constructing DataFrame from Series/DataFrame:

>>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>> df = pd.DataFrame(data=ser, index=["a", "c"])
>>> df
   0
a  1
c  3

>>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])
>>> df2 = pd.DataFrame(data=df1, index=["a", "c"])
>>> df2
   x
a  1
c  3

A pandas.DataFrame of EUROCONTROL Member / Agreement States.

Columns:

Column	Description
`name`	Country name (e.g. `"Italy"`)
`iso3c`	ISO 3166-1 alpha-3 code (e.g. `"ITA"`)
`iso2c`	ISO 3166-1 alpha-2 code (e.g. `"IT"`) — used by `airlines_tidy()`
`icao`	ICAO 2-letter prefix (e.g. `"LI"`)
`iso3n`	ISO 3166-1 numeric code (e.g. `"380"`)
`date`	Date of membership status change
`status`	`"M"` (Member), `"C"` (Comprehensive Agreement), or `NaN` (Kosovo)

Note

aircraft_type and aircraft_model datasets from the R package are not bundled here due to size and update frequency. Fetch them directly from the ICAO Aircraft Type Designators list or supply your own CSV.