P1 {'amphibolite.txt': 0.00593448307396262, 'basalt.txt': 0.00513281304394720, 'breccia.txt': 0.00501607343520665}
P2 {}
P3 {'amphibolite.txt': 0.00484181658252891, 'siltstone.txt': 0.00486999982527626}
P4 {'amphibolite.txt': 0.00489936166850968, 'siltstone.txt': 0.00494558784770914}
[{'amphibolite.txt': 0.00593448307396262, 'basalt.txt': 0.00513281304394720, 'breccia.txt': 0.00501607343520665}, {}, {'amphibolite.txt': 0.00484181658252891, 'siltstone.txt': 0.00486999982527626}, {'amphibolite.txt': 0.00489936166850968, 'siltstone.txt': 0.00494558784770914}]
Below is a combination of dictionary files from P1 to P4 as inventory. Among the values that come out like that, I would like to express it as one key by adding the same values in a dictionary with the same key value, such as amphibolite.txt. Help me
python dictionary
Pandas are very comfortable.
>>> import pandas as pd
>>> df = pd.DataFrame([{'amphibolite.txt': 0.00593448307396262, 'basalt.txt': 0.00513281304394720, 'breccia.txt': 0.00501607343520665}, {}, {'amphibolite.txt': 0.00484181658252891, 'siltstone.txt': 0.00486999982527626}, {'amphibolite.txt': 0.00489936166850968, 'siltstone.txt': 0.00494558784770914}])
>>> df
amphibolite.txt basalt.txt breccia.txt siltstone.txt
0 0.005934 0.005133 0.005016 NaN
1 NaN NaN NaN NaN
2 0.004842 NaN NaN 0.004870
3 0.004899 NaN NaN 0.004946
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
amphibolite.txt 3 non-null float64
basalt.txt 1 non-null float64
breccia.txt 1 non-null float64
siltstone.txt 2 non-null float64
dtypes: float64(4)
memory usage: 208.0 bytes
>>> df.describe()
amphibolite.txt basalt.txt breccia.txt siltstone.txt
count 3.000000 1.000000 1.000000 2.000000
mean 0.005225 0.005133 0.005016 0.004908
std 0.000615 NaN NaN 0.000053
min 0.004842 0.005133 0.005016 0.004870
25% 0.004871 0.005133 0.005016 0.004889
50% 0.004899 0.005133 0.005016 0.004908
75% 0.005417 0.005133 0.005016 0.004927
max 0.005934 0.005133 0.005016 0.004946
>>> df.sum()
amphibolite.txt 0.015676
basalt.txt 0.005133
breccia.txt 0.005016
siltstone.txt 0.009816
dtype: float64
>>> df.product()
amphibolite.txt 1.407767e-07
basalt.txt 5.132813e-03
breccia.txt 5.016073e-03
siltstone.txt 2.408501e-05
dtype: float64
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