[英]how to change the index value of numpy array with column values of pandas dataframe
[英]Update numpy array based on nearest value in pandas DataFrame column
如何根據 pandas DataFrame 列中最接近的值更新數組? 例如,我想根據 pandas DataFrame 中的“時間”列更新以下數組,以便該數組現在包含“X”值:
輸入數組:
a = np.array([
[122.25, 225.00, 201.00],
[125.00, 151.50, 160.62],
[99.99, 142.25, 250.01],
])
輸入 DataFrame:
df = pd.DataFrame({
'Time': [100, 125, 150, 175, 200, 225],
'X': [26100, 26200, 26300, 26000, 25900, 25800],
})
預期 output 數組:
([
[26200, 25800, 25900],
[26200, 26300, 26300],
[26100, 26300, 25800],
])
使用merge_asof
:
# Convert Time to float since your input array is float.
# merge_asof requires both sides to have the same data types
df['Time'] = df['Time'].astype('float')
# merge_asof also requires both data frames to be sorted by the join key (Time)
# So we need to flatten the input array and make note of the original order
# before going into the merge
a_ = np.ravel(a)
o_ = np.arange(len(a_))
tmp = pd.DataFrame({
'Time': a_,
'Order': o_
})
# Merge the two data frames and extract X in the original order
result = (
pd.merge_asof(tmp.sort_values('Time'), df.sort_values('Time'), on='Time', direction='nearest')
.sort_values('Order')
['X'].to_numpy()
.reshape(a.shape)
)
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