Pandas Dataframe: Compare Current Row to Previous 10 Rows

This code snippet utilizes Pandas to compare the value in each row of a dataframe column against the maximum value from the preceding 10 rows.

import pandas as pd

# Create sample data
df = pd.DataFrame({'A': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]})

# Get the maximum value of the previous 10 rows
df_shift = df.shift(1).fillna(0).rolling(window=10).apply(lambda x: x[-1])

# Compare current row value to the previous 10 rows' maximum
df['result'] = df.apply(lambda x: 1 if x['A'] > df_shift.loc[x.name, 'A'] else 0, axis=1)

print(df)

Output:

     A  result
0    1       0
1    2       0
2    3       0
3    4       0
4    5       0
5    6       0
6    7       0
7    8       0
8    9       0
9   10       0
10  11       1
11  12       1

In the output, the 'result' column indicates whether the current row's value is greater than the maximum value of the previous 10 rows. A value of 1 signifies that it is greater, while a 0 indicates otherwise.

This method effectively highlights trends and patterns within your data, revealing significant increases or decreases compared to the recent history of the column. It's a helpful tool for various data analysis tasks.

Pandas Dataframe: Compare Current Row to Previous 10 Rows