Identifying which Time-series column grew within a pandas DataFrame based on boolean conditions
1) My Pandas dataframe consists of a panel of timeseries (C1,C2,C3.
2)The time-series are indexed from most recent period "5" to 4 days ago "0".
3) I want to create a column that identifies only rows were only 1 time-series had a unique maximum. Once this row is identified that time-series will be compared to its two previous lags to see if it's current value is larger than it's lagged values (i.e. shift(-1) and shift(-2)). Also for growth to have occurred shift(-1) > shift(-2).
4) For example,Gwth_Col (Growth Column) is 1 (C1) for the first value. C1 is the largest value on the current row and it is larger than its 1 period lag and it's 1 period lag is larger than it's 2 period lag.
I understand pieces of the puzzle, thanks to Stackoverflow contributors, but I can't gain traction on pulling it together to crate a flexible function or apply function to calculate this new column. Thanks for any help, much appreciate it!
# Find out which unique exhibited growth in the time series.
# create a dictionary
dict = {
'C1': [9,7,6,6,7,8],
'C2': [8,8,7,6,5,6],
'C3': [5,5,3,2,1,7],
'Gwth_Col':[1,2,2,0,0,0]
}
# create a list of strings
columns = ['C1', 'C2','C3','Gwth_Col']
index = [5,4,3,2,1,0]
# Passing a dictionary
# key: column name
# value: series of values
df = pd.DataFrame(dict, columns=columns, index=index)
df
python pandas time-series list-comprehension boolean-logic
asked Nov 12 '18 at 22:23
rer49rer49
415
add a comment |
1) My Pandas dataframe consists of a panel of timeseries (C1,C2,C3.
2)The time-series are indexed from most recent period "5" to 4 days ago "0".
3) I want to create a column that identifies only rows were only 1 time-series had a unique maximum. Once this row is identified that time-series will be compared to its two previous lags to see if it's current value is larger than it's lagged values (i.e. shift(-1) and shift(-2)). Also for growth to have occurred shift(-1) > shift(-2).
4) For example,Gwth_Col (Growth Column) is 1 (C1) for the first value. C1 is the largest value on the current row and it is larger than its 1 period lag and it's 1 period lag is larger than it's 2 period lag.
I understand pieces of the puzzle, thanks to Stackoverflow contributors, but I can't gain traction on pulling it together to crate a flexible function or apply function to calculate this new column. Thanks for any help, much appreciate it!
# Find out which unique exhibited growth in the time series.
# create a dictionary
dict = {
'C1': [9,7,6,6,7,8],
'C2': [8,8,7,6,5,6],
'C3': [5,5,3,2,1,7],
'Gwth_Col':[1,2,2,0,0,0]
}
# create a list of strings
columns = ['C1', 'C2','C3','Gwth_Col']
index = [5,4,3,2,1,0]
# Passing a dictionary
# key: column name
# value: series of values
df = pd.DataFrame(dict, columns=columns, index=index)
df
python pandas time-series list-comprehension boolean-logic
asked Nov 12 '18 at 22:23
rer49rer49
415
add a comment |
1) My Pandas dataframe consists of a panel of timeseries (C1,C2,C3.
2)The time-series are indexed from most recent period "5" to 4 days ago "0".
3) I want to create a column that identifies only rows were only 1 time-series had a unique maximum. Once this row is identified that time-series will be compared to its two previous lags to see if it's current value is larger than it's lagged values (i.e. shift(-1) and shift(-2)). Also for growth to have occurred shift(-1) > shift(-2).
4) For example,Gwth_Col (Growth Column) is 1 (C1) for the first value. C1 is the largest value on the current row and it is larger than its 1 period lag and it's 1 period lag is larger than it's 2 period lag.
I understand pieces of the puzzle, thanks to Stackoverflow contributors, but I can't gain traction on pulling it together to crate a flexible function or apply function to calculate this new column. Thanks for any help, much appreciate it!
# Find out which unique exhibited growth in the time series.
# create a dictionary
dict = {
'C1': [9,7,6,6,7,8],
'C2': [8,8,7,6,5,6],
'C3': [5,5,3,2,1,7],
'Gwth_Col':[1,2,2,0,0,0]
}
# create a list of strings
columns = ['C1', 'C2','C3','Gwth_Col']
index = [5,4,3,2,1,0]
# Passing a dictionary
# key: column name
# value: series of values
df = pd.DataFrame(dict, columns=columns, index=index)
df
python pandas time-series list-comprehension boolean-logic
asked Nov 12 '18 at 22:23
rer49rer49
415
1) My Pandas dataframe consists of a panel of timeseries (C1,C2,C3.
2)The time-series are indexed from most recent period "5" to 4 days ago "0".
3) I want to create a column that identifies only rows were only 1 time-series had a unique maximum. Once this row is identified that time-series will be compared to its two previous lags to see if it's current value is larger than it's lagged values (i.e. shift(-1) and shift(-2)). Also for growth to have occurred shift(-1) > shift(-2).
4) For example,Gwth_Col (Growth Column) is 1 (C1) for the first value. C1 is the largest value on the current row and it is larger than its 1 period lag and it's 1 period lag is larger than it's 2 period lag.
I understand pieces of the puzzle, thanks to Stackoverflow contributors, but I can't gain traction on pulling it together to crate a flexible function or apply function to calculate this new column. Thanks for any help, much appreciate it!
# Find out which unique exhibited growth in the time series.
# create a dictionary
dict = {
'C1': [9,7,6,6,7,8],
'C2': [8,8,7,6,5,6],
'C3': [5,5,3,2,1,7],
'Gwth_Col':[1,2,2,0,0,0]
}
# create a list of strings
columns = ['C1', 'C2','C3','Gwth_Col']
index = [5,4,3,2,1,0]
# Passing a dictionary
# key: column name
# value: series of values
df = pd.DataFrame(dict, columns=columns, index=index)
df
python pandas time-series list-comprehension boolean-logic
python pandas time-series list-comprehension boolean-logic
asked Nov 12 '18 at 22:23
rer49rer49
415
asked Nov 12 '18 at 22:23
rer49rer49
415
asked Nov 12 '18 at 22:23
rer49rer49
415
asked Nov 12 '18 at 22:23
rer49rer49
415
asked Nov 12 '18 at 22:23
rer49rer49
415
415
add a comment |
add a comment |
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