diff --git a/lib/iris/pandas.py b/lib/iris/pandas.py index 1b4e444efc..1bf9509d1b 100644 --- a/lib/iris/pandas.py +++ b/lib/iris/pandas.py @@ -678,6 +678,9 @@ def as_data_frame( -------- >>> import iris >>> from iris.pandas import as_data_frame + >>> import pandas as pd + >>> pd.set_option('display.width', 1000) + >>> pd.set_option('display.max_columns', 1000) Convert a simple :class:`~iris.cube.Cube`: @@ -708,19 +711,19 @@ def as_data_frame( >>> df = as_data_frame(cube, add_aux_coords=True) >>> print(df) ... # doctest: +NORMALIZE_WHITESPACE - surface_temperature ... forecast_reference_time - time latitude longitude ... - 2006-04-16 00:00:00 -4.999992 0.000000 301.659271 ... 2006-04-16 12:00:00 - 0.833333 301.785004 ... 2006-04-16 12:00:00 - 1.666667 301.820984 ... 2006-04-16 12:00:00 - 2.500000 301.865234 ... 2006-04-16 12:00:00 - 3.333333 301.926819 ... 2006-04-16 12:00:00 - ... ... ... ... - 2010-09-16 00:00:00 4.444450 355.833313 298.779938 ... 2010-09-16 12:00:00 - 356.666656 298.913147 ... 2010-09-16 12:00:00 - 357.500000 NaN ... 2010-09-16 12:00:00 - 358.333313 NaN ... 2010-09-16 12:00:00 - 359.166656 298.995148 ... 2010-09-16 12:00:00 + surface_temperature forecast_period forecast_reference_time + time latitude longitude + 2006-04-16 00:00:00 -4.999992 0.000000 301.659271 0 2006-04-16 12:00:00 + 0.833333 301.785004 0 2006-04-16 12:00:00 + 1.666667 301.820984 0 2006-04-16 12:00:00 + 2.500000 301.865234 0 2006-04-16 12:00:00 + 3.333333 301.926819 0 2006-04-16 12:00:00 + ... ... ... ... + 2010-09-16 00:00:00 4.444450 355.833313 298.779938 0 2010-09-16 12:00:00 + 356.666656 298.913147 0 2010-09-16 12:00:00 + 357.500000 NaN 0 2010-09-16 12:00:00 + 358.333313 NaN 0 2010-09-16 12:00:00 + 359.166656 298.995148 0 2010-09-16 12:00:00 [419904 rows x 3 columns] @@ -730,19 +733,19 @@ def as_data_frame( >>> df['STASH'] = str(cube.attributes['STASH']) >>> print(df) ... # doctest: +NORMALIZE_WHITESPACE - surface_temperature ... STASH - time latitude longitude ... - 2006-04-16 00:00:00 -4.999992 0.000000 301.659271 ... m01s00i024 - 0.833333 301.785004 ... m01s00i024 - 1.666667 301.820984 ... m01s00i024 - 2.500000 301.865234 ... m01s00i024 - 3.333333 301.926819 ... m01s00i024 - ... ... ... ... - 2010-09-16 00:00:00 4.444450 355.833313 298.779938 ... m01s00i024 - 356.666656 298.913147 ... m01s00i024 - 357.500000 NaN ... m01s00i024 - 358.333313 NaN ... m01s00i024 - 359.166656 298.995148 ... m01s00i024 + surface_temperature forecast_period forecast_reference_time STASH + time latitude longitude + 2006-04-16 00:00:00 -4.999992 0.000000 301.659271 0 2006-04-16 12:00:00 m01s00i024 + 0.833333 301.785004 0 2006-04-16 12:00:00 m01s00i024 + 1.666667 301.820984 0 2006-04-16 12:00:00 m01s00i024 + 2.500000 301.865234 0 2006-04-16 12:00:00 m01s00i024 + 3.333333 301.926819 0 2006-04-16 12:00:00 m01s00i024 + ... ... ... ... ... + 2010-09-16 00:00:00 4.444450 355.833313 298.779938 0 2010-09-16 12:00:00 m01s00i024 + 356.666656 298.913147 0 2010-09-16 12:00:00 m01s00i024 + 357.500000 NaN 0 2010-09-16 12:00:00 m01s00i024 + 358.333313 NaN 0 2010-09-16 12:00:00 m01s00i024 + 359.166656 298.995148 0 2010-09-16 12:00:00 m01s00i024 [419904 rows x 4 columns] @@ -753,18 +756,18 @@ def as_data_frame( >>> df.reset_index(inplace=True) >>> print(df) ... # doctest: +NORMALIZE_WHITESPACE - time latitude ... forecast_reference_time STASH - 0 2006-04-16 00:00:00 -4.999992 ... 2006-04-16 12:00:00 m01s00i024 - 1 2006-04-16 00:00:00 -4.999992 ... 2006-04-16 12:00:00 m01s00i024 - 2 2006-04-16 00:00:00 -4.999992 ... 2006-04-16 12:00:00 m01s00i024 - 3 2006-04-16 00:00:00 -4.999992 ... 2006-04-16 12:00:00 m01s00i024 - 4 2006-04-16 00:00:00 -4.999992 ... 2006-04-16 12:00:00 m01s00i024 - ... ... ... ... ... ... - 419899 2010-09-16 00:00:00 4.444450 ... 2010-09-16 12:00:00 m01s00i024 - 419900 2010-09-16 00:00:00 4.444450 ... 2010-09-16 12:00:00 m01s00i024 - 419901 2010-09-16 00:00:00 4.444450 ... 2010-09-16 12:00:00 m01s00i024 - 419902 2010-09-16 00:00:00 4.444450 ... 2010-09-16 12:00:00 m01s00i024 - 419903 2010-09-16 00:00:00 4.444450 ... 2010-09-16 12:00:00 m01s00i024 + time latitude longitude surface_temperature forecast_period forecast_reference_time STASH + 0 2006-04-16 00:00:00 -4.999992 0.000000 301.659271 0 2006-04-16 12:00:00 m01s00i024 + 1 2006-04-16 00:00:00 -4.999992 0.833333 301.785004 0 2006-04-16 12:00:00 m01s00i024 + 2 2006-04-16 00:00:00 -4.999992 1.666667 301.820984 0 2006-04-16 12:00:00 m01s00i024 + 3 2006-04-16 00:00:00 -4.999992 2.500000 301.865234 0 2006-04-16 12:00:00 m01s00i024 + 4 2006-04-16 00:00:00 -4.999992 3.333333 301.926819 0 2006-04-16 12:00:00 m01s00i024 + ... ... ... ... ... ... ... + 419899 2010-09-16 00:00:00 4.444450 355.833313 298.779938 0 2010-09-16 12:00:00 m01s00i024 + 419900 2010-09-16 00:00:00 4.444450 356.666656 298.913147 0 2010-09-16 12:00:00 m01s00i024 + 419901 2010-09-16 00:00:00 4.444450 357.500000 NaN 0 2010-09-16 12:00:00 m01s00i024 + 419902 2010-09-16 00:00:00 4.444450 358.333313 NaN 0 2010-09-16 12:00:00 m01s00i024 + 419903 2010-09-16 00:00:00 4.444450 359.166656 298.995148 0 2010-09-16 12:00:00 m01s00i024 [419904 rows x 7 columns]