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Index.get_level_values (self, level) Parameters. level - It is either the integer position or the name of the level. Examples: import pandas as pd import numpy as np levels = ['foo', 'bar', 'baz', 'qux'] codes = np.random.randint(0, 4, size=100) cats = pd.Categorical.from_codes(codes, levels, name='myfactor', ordered=True) data = pd.DataFrame(np.random.randn(100, 4)) grouped = data.groupby(cats) desc_result = grouped.describe() desc_result.index.get_level_values(0) # AttributeError: 'Categorical' object has no attribute 'flags' When an index is first created, a starting (base) value is chosen. In our example, we will use 100 as the base value. Now that we have the total market value of our index and our base value, the next step is to determine the index divisor by dividing the total market value of the index by the base index value of 100 ($970 / 100 = 9.7). level = i: level_values = axis_index. get_level_values (level) s = level_values.

Index get level values

0.85. OMX Stockholm 30 Index, 2,265.66. 22.67. 1.01.

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Throws: NullPointerException - if the name is null IllegalArgumentException - if the value is not valid. Valid values are integers between Integer.MIN_VALUE and Integer.MAX_VALUE, and all known level names.

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Out[11]:. C. A B. 0 one 3. 1 one 2. 2 two 1. In [12]: df. index.levels[1]. Out[12]: Index([one, two], dtype=object) The Categories and Value Ranges in Table 1 apply to the following Indices/IDs: Daily Index Categories and Level Values.

Aim: To determine optimum settings of ATCM noise index (NI) together with for a general electric (GE) scanner that aims to achieve similar image quality to the Noise contour A line of constant value of a cumulative aircraft noise level or Two of our most useful REACH registered product developments are Petrit® E and Petrit® T, which are both made of slag. Petrit E can be used to replace gravel The essential meaning of this proposition is that the economic values that that economic values at the macroeconomic level are fundamentally determined by the rather than economic activity are more likely to achieve absolute decoupling.
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Syntax: Index.values. Parameter : None. Returns : … 2021-4-9 · 0: To get the sum of values along the index/rows; 1: To get the sum of values along the columns; skipna: bool, the default value is True.

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eastern Baltic Sea - ICES

Index.isin (values[, level]) Return a boolean array where the index values are in values. In [1]: import numpy as np In [2]: import pandas as pd In [3]: index = pd.MultiIndex.from_arrays([ ['a', 'b', 'b'], [1, np.nan, 2] ]) In [4]: index.get_level_values(1) Out[4]: Float64Index([1.0, 2.0, 2.0], dtype=object) The expected output is Float64Index([1.0, nan, 2.0], dtype=object) import pandas as pd import numpy as np levels = ['foo', 'bar', 'baz', 'qux'] codes = np.random.randint(0, 4, size=100) cats = pd.Categorical.from_codes(codes, levels, name='myfactor', ordered=True) data = pd.DataFrame(np.random.randn(100, 4)) grouped = data.groupby(cats) desc_result = grouped.describe() desc_result.index.get_level_values(0) # AttributeError: 'Categorical' object has no attribute 'flags' level = i: level_values = axis_index.