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It will return an Index of values for the requested level. This is primarily useful to get an individual level of values from a MultiIndex, but is provided on Index as well for compatibility. Index.get_level_values (self, level) Parameters. level - It is either the integer position or the name of the level. Examples: This happens because NA values are not stored in the MultiIndex levels and the corresponding label is set to -1. Then when labels are used as indexes to values in get_level_values() that -1 points to the last (not null) value. I tried to fix this by appending a NA to the values if -1 is in levels.

wave temp fix.. 5 aug. 2019 — I just can't get this to work, I've tried with loops and the code below, any help is appreciated. Kind Regards! Function Create_array ($index, $value) 32 sidor · 736 kB — the event.

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to_series s. index = axis_index: d [key] = s # put the index/columns itself in the dict: if isinstance (axis_index, MultiIndex): dindex = axis_index: else: dindex = axis_index.

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df.index.get_level_values(['a','c']) There's a workaround in which one could use get_level_values over each desired column and zip … Index.get_level_values (level) Return an Index of values for requested level. Index.get_loc (key[, method, tolerance]) Get integer location, slice or boolean mask for requested label. Index.get_slice_bound (label, side, kind) Calculate slice bound that corresponds to given label. Index.get_value (series, key) Fast lookup of value from 1 To extract a specific value you can use xs (cross-section): In [18]: df.xs (key=0.9027639999999999) Out [18]: C B -0.259656 -1.864541 In [19]: df.xs (key=0.9027639999999999, drop_level=False) Out [19]: C A B 0.902764 -0.259656 -1.864541. PDF - Download pandas for free. Previous Next.

I can do: df = df.reset_index () uniq_b = df.B.unique () df = df.set_index ( ['A','B']) 2020-09-03 2017-04-27 2015-06-28 2013-10-01 In [108]: s. index. set_names (["L1", "L2"], inplace = True) In [109]: s.
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Returns: values: Index. self, as there is only one level in the Index. See also. An alternative approach is to find the number of levels by calling df.index.levels[level_index] where level_index can be inferred from df.index.names.index(level_name). In the above example level_name = 'co'.

swaplevel ([i, j]) Swap level i with level j. For example, if Index A had a base value of 100 in January of 2015 and that value increased to 150 as of January 2018, the index value increased by 50% over that 3-year period. Index B measures the exact same market, but its starting base value was 1,000 in January of 2015, and its value grew to 1,500 as of January 2018. Dismiss Join GitHub today.
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If both row_num and col_num are supplied, INDEX returns the value in the cell at the intersection of row_num and col_num. If row_num is set to zero, INDEX returns an array of values for an entire column. To use these array values, you can enter the INDEX function as an array formula in horizontal range, or feed the array into another function.

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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.