I'm trying to set a series into another one, at a multi-index value. I cannot find a way to do it in Pandas without complex hacks.
My original series:
one 1 0.522764
3 0.362663
7 0.963108
two 2 0.717855
4 0.004645
5 0.077471
The data I want to concatenate, at level three
:
2 0.8
7 0.9
8 0.7
The desired output:
one 1 0.522764
3 0.362663
7 0.963108
two 2 0.717855
4 0.004645
5 0.077471
three 2 0.800000
7 0.900000
8 0.700000
I cannot figure out an elegant way to do this in pandas. All I've been able to do is the following hack:
# imports
import numpy as np
import pandas as pd
# to replicate the Series:
np.arrays = [['one','one','one','two','two','two'],[1,3,7,2,4,5]]
my_series = pd.Series([np.random.random() for i in range(6)],
index=pd.MultiIndex.from_tuples(list(zip(*np.arrays))))
# the new data I need to add:
new_data = pd.Series({1: .9, 2: .7, 3: .8})
Here is how I'm currently solving it:
# rename the index so that I can call it later
new_data.index.name = 'level_1'
# turn it into temporary a dataframe so that I can add a new column
temp = pd.DataFrame(new_data)
# create a new column with the desired name for first index level
temp['level_0'] = 'three'
# reset index, set the new index, turn into Series again
temp = temp.reset_index().set_index(['level_0', 'level_1'])[0]
# append it to the larger dataframe
my_series = my_series.append(temp)
This yields the desired output.
Question: Is there a simple, elegant way to do this in Pandas?
You could try using pd.concat
:
u = (new_data.to_frame()
.assign(_='three')
.set_index(['_', new_data.index])[0])
pd.concat([df, u])
one 1 0.618472
3 0.026207
7 0.766849
two 2 0.651633
4 0.282038
5 0.160714
three 1 0.900000
2 0.700000
3 0.800000
dtype: float64
Option 1
pd.concat
is a handy way to prepend an index or column level by using the keys
argument. Combined this with a second pd.concat
to get the job done.
pd.concat([my_series, pd.concat([new_data], keys=['Three'])])
one 1 0.943246
3 0.412200
7 0.379641
two 2 0.883960
4 0.182983
5 0.773227
Three 1 0.900000
2 0.700000
3 0.800000
dtype: float64
Option 2
Or we can construct a new series while inserting an additional array into the index
argument. Using pd.concat
again to combine. Note I could have used pd.MultiIndex.from_arrays
but the syntax is simplified by just passing the arrays directly to the index
argument.
pd.concat([
my_series,
pd.Series(new_data.values, [['Three'] * new_data.size, new_data.index])
])
one 1 0.943246
3 0.412200
7 0.379641
two 2 0.883960
4 0.182983
5 0.773227
Three 1 0.900000
2 0.700000
3 0.800000
dtype: float64
Option 3
Yet another way to reconstruct a series with a multiindex. This one uses pd.MultiIndex.from_product
.
pd.concat([
my_series,
pd.Series(new_data.values, pd.MultiIndex.from_product([['Three'], new_data.index]))
])
one 1 0.943246
3 0.412200
7 0.379641
two 2 0.883960
4 0.182983
5 0.773227
Three 1 0.900000
2 0.700000
3 0.800000
dtype: float64
If you start out with an equivalent multi-index for new_data
, you can concatenate the Series
es directly with pd.concat
without coercing to DataFrame
and back, as in:
new_series = pd.Series([0.8,0.9,0.7],
index=pd.MultiIndex.from_tuples([('three',x) for x in range(1,4)])
)
pd.concat([my_series,new_series]) #note OP changed name of orig series from df to my_series
#==============================================================================
# one 1 0.236158
# 3 0.699102
# 7 0.421937
# two 2 0.887081
# 4 0.520304
# 5 0.211461
# three 1 0.800000
# 2 0.900000
# 3 0.700000
# dtype: float64
#==============================================================================
type(pd.concat([my_series,new_series])) # pandas.core.series.Series
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