I'm working with a large data frame and I'm struggling to find an efficient way to eliminate specific dates. Note that I'm trying to eliminate any measurements from a specific date.
Pandas has this great function, where you can call:
df.ix['2016-04-22']
and pull all rows from that day. But what if I want to eliminate all rows from '2016-04-22'?
I want a function like this:
df.ix[~'2016-04-22']
(but that doesn't work)
Also, what if I want to eliminate a list of dates?
Right now, I have the following solution:
import numpy as np
import pandas as pd
from numpy import random
###Create a sample data frame
dates = [pd.Timestamp('2016-04-25 06:48:33'), pd.Timestamp('2016-04-27 15:33:23'), pd.Timestamp('2016-04-23 11:23:41'), pd.Timestamp('2016-04-28 12:08:20'), pd.Timestamp('2016-04-21 15:03:49'), pd.Timestamp('2016-04-23 08:13:42'), pd.Timestamp('2016-04-27 21:18:22'), pd.Timestamp('2016-04-27 18:08:23'), pd.Timestamp('2016-04-27 20:48:22'), pd.Timestamp('2016-04-23 14:08:41'), pd.Timestamp('2016-04-27 02:53:26'), pd.Timestamp('2016-04-25 21:48:31'), pd.Timestamp('2016-04-22 12:13:47'), pd.Timestamp('2016-04-27 01:58:26'), pd.Timestamp('2016-04-24 11:48:37'), pd.Timestamp('2016-04-22 08:38:46'), pd.Timestamp('2016-04-26 13:58:28'), pd.Timestamp('2016-04-24 15:23:36'), pd.Timestamp('2016-04-22 07:53:46'), pd.Timestamp('2016-04-27 23:13:22')]
values = random.normal(20, 20, 20)
df = pd.DataFrame(index=dates, data=values, columns ['values']).sort_index()
### This is the list of dates I want to remove
removelist = ['2016-04-22', '2016-04-24']
This for loop basically grabs the index for the dates I want to remove, then eliminates it from the index of the main dataframe, then positively selects the remaining dates (ie: the good dates) from the dataframe.
for r in removelist:
elimlist = df.ix[r].index.tolist()
ind = df.index.tolist()
culind = [i for i in ind if i not in elimlist]
df = df.ix[culind]
Is there anything better out there?
I've also tried indexing by the rounded date+1 day, so something like this:
df[~((df['Timestamp'] < r+pd.Timedelta("1 day")) & (df['Timestamp'] > r))]
But this gets really cumbersome and (at the end of the day) I'll still be using a for loop when I need to eliminate n specific dates.
There's got to be a better way! Right? Maybe?
Same idea as @Alexander, but using properties of the DatetimeIndex and numpy.in1d:
mask = ~np.in1d(df.index.date, pd.to_datetime(removelist).date)
df = df.loc[mask, :]
Timings:
%timeit df.loc[~np.in1d(df.index.date, pd.to_datetime(removelist).date), :]
1000 loops, best of 3: 1.42 ms per loop
%timeit df[[d.date() not in pd.to_datetime(removelist) for d in df.index]]
100 loops, best of 3: 3.25 ms per loop