Reputation: 49
Right now, my data frame has two columns: a DateTimeIndex and a Load column. I want to add a third column with a consecutive second count, from zero, based on the DateTimeIndex.
import pandas as pd
import matplotlib.pyplot as plt
from scipy import signal
import numpy as np
# Create sample Data
df = pd.DataFrame([['2020-07-25 09:26:28',2],['2020-07-25 09:26:29',10],['2020-07-25 09:26:32',203],['2020-07-25 09:26:33',30]],
columns = ['Time','Load'])
df['Time'] = pd.to_datetime(df['Time'])
df = df.set_index("Time")
rng = pd.date_range(df.index[0], df.index[-1], freq='s')
df = df.reindex(rng).fillna(0)
## Create Elapsed Seconds Timeseries from DateTimeIndex
ts = pd.Series(df.index(range(len(df.index)), index=df.index))
# Desired Output
Load CountS
2020-07-25 09:26:28 2.0 1
2020-07-25 09:26:29 10.0 2
2020-07-25 09:26:30 0.0 3
2020-07-25 09:26:31 0.0 4
2020-07-25 09:26:32 203.0 5
2020-07-25 09:26:33 30.0 6
# Actual Output
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-32-02bfe0dcc12d> in <module>
17 ## Create Elapsed Seconds Column from DateTimeIndex
18
---> 19 ts = pd.Series(df.index(range(len(df.index)), index=df.index))
20
21 # df["Seconds"] =
TypeError: 'DatetimeIndex' object is not callable
Upvotes: 0
Views: 680
Reputation: 49
In case anyone else is asking a similar question to mine in a similarly confusing way (sorry, longtime users; I am still learning to ask questions better), here is the code that elegantly does what I want.
# Change datetimeindex to timedelta by subtracting to datetimeindices.
# Change to integers by appending .seconds to datetime
# Assign values to new column "count"
df["Count"] = (df.index - df_index[0]).seconds
Upvotes: 0
Reputation: 6091
seems like the issue is the instruction
df.index(range(len(df.index))
you're using df.index()
and that might be raising the not callable error (simple way to look at it: parenthesis are for methods, brackets are for indexing). If you want to use a slice of df.index use the syntax df.index[]
. Since it is not clear what you want to achieve I can't recommend a better solution
UPDATE:
after looking at your desired output, you can achieve that by doing
df.asfreq('s').fillna(0)
Output:
Load
Time
2020-07-25 09:26:28 2.0
2020-07-25 09:26:29 10.0
2020-07-25 09:26:30 0.0
2020-07-25 09:26:31 0.0
2020-07-25 09:26:32 203.0
2020-07-25 09:26:33 30.0
And regarding the seconds, there might be a simpler way, but this is what I have for you:
df['CountS'] = df.index.to_series().diff().astype('timedelta64[s]').fillna(0).cumsum() + 1
Load CountS
Time
2020-07-25 09:26:28 2.0 1.0
2020-07-25 09:26:29 10.0 2.0
2020-07-25 09:26:30 0.0 3.0
2020-07-25 09:26:31 0.0 4.0
2020-07-25 09:26:32 203.0 5.0
2020-07-25 09:26:33 30.0 6.0
Upvotes: 1