Need to extract sequence of negative values where earlier negative value is smaller than current value and next value is smaller than current value
import pandas as pd
# Create the DataFrame with the given values
data = {
'Value': [0.3, 0.2, 0.1, -0.1, -0.2, -0.3, -0.4, -0.35, -0.25, 0.1, -0.15, -0.25, -0.13, -0.1, 1]
}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)
My Code:
# Initialize a list to hold the sequences
sequences = []
current_sequence = []
# Iterate through the DataFrame to apply the condition
for i in range(1, len(df) - 1):
prev_value = df.loc[i - 1, 'Value']
curr_value = df.loc[i, 'Value']
next_value = df.loc[i + 1, 'Value']
# Check the condition
if curr_value < prev_value and curr_value < next_value:
current_sequence.append(curr_value)
else:
# If the current sequence is not empty and it's a valid sequence, add it to sequences list and reset
if current_sequence:
sequences.append(current_sequence)
current_sequence = []
# Add the last sequence if it's not empty
if current_sequence:
sequences.append(current_sequence)
My Output:
Extracted Sequences:
[-0.4]
[-0.25]
Expected Output:
[-0.1,-0.2,-0.3,-0.4]
[-0.15,-0.25]
You can build masks to identify the negative values and consecutive decreasing values and use groupby to split:
# is the value negative?
m1 = df['Value'].lt(0)
# is the value decreasing?
m2 = df['Value'].diff().le(0)
m = m1&m2
# aggregate
out = df[m].groupby((~m).cumsum())['Value'].agg(list).tolist()
Output:
[[-0.1, -0.2, -0.3, -0.4], [-0.15, -0.25]]
If you just want to filter:
out = df[m]
Output:
Value
3 -0.10
4 -0.20
5 -0.30
6 -0.40
10 -0.15
11 -0.25
Intermediates:
Value m1 df['Value'].diff() m2 m1&m2
0 0.30 False NaN False False
1 0.20 False -0.10 True False
2 0.10 False -0.10 True False
3 -0.10 True -0.20 True True
4 -0.20 True -0.10 True True
5 -0.30 True -0.10 True True
6 -0.40 True -0.10 True True
7 -0.35 True 0.05 False False
8 -0.25 True 0.10 False False
9 0.10 False 0.35 False False
10 -0.15 True -0.25 True True
11 -0.25 True -0.10 True True
12 -0.13 True 0.12 False False
13 -0.10 True 0.03 False False
14 1.00 False 1.10 False False