I have a dataframe that looks like this:
open high low close weekday
time
2011-11-29 2.55 2.98 2.54 2.75 1
2011-11-30 2.75 3.09 2.73 2.97 2
2011-12-01 2.97 3.14 2.93 3.06 3
2011-12-02 3.06 3.14 3.03 3.12 4
2011-12-03 3.12 3.13 2.75 2.79 5
2011-12-04 2.79 2.90 2.61 2.83 6
2011-12-05 2.83 2.93 2.78 2.88 0
2011-12-06 2.88 3.05 2.87 3.03 1
2011-12-07 3.03 3.08 2.93 2.99 2
2011-12-08 2.99 3.01 2.88 2.98 3
2011-12-09 2.98 3.04 2.93 2.97 4
2011-12-10 2.97 3.13 2.93 3.05 5
2011-12-11 3.05 3.38 2.99 3.25 6
The weekday column refers to 0 = Monday,...6 = Sunday.
I want to make groups of smaller dataframes only containing the data for Friday, Saturday, Sunday and Monday. So one subset would look like this:
2011-12-02 3.06 3.14 3.03 3.12 4
2011-12-03 3.12 3.13 2.75 2.79 5
2011-12-04 2.79 2.90 2.61 2.83 6
2011-12-05 2.83 2.93 2.78 2.88 0
filter before drop_duplicates
df[df.weekday.isin([4,5,6,0])].drop_duplicates('weekday')
Out[10]:
open high low close weekday
2011-12-02 3.06 3.14 3.03 3.12 4
2011-12-03 3.12 3.13 2.75 2.79 5
2011-12-04 2.79 2.90 2.61 2.83 6
2011-12-05 2.83 2.93 2.78 2.88 0
Related
I have this dataframe df:
alpha1 week_day calendar_week
0 2.49 Freitag 2022-04-(01/07)
1 1.32 Samstag 2022-04-(01/07)
2 2.70 Sonntag 2022-04-(01/07)
3 3.81 Montag 2022-04-(01/07)
4 3.58 Dienstag 2022-04-(01/07)
5 3.48 Mittwoch 2022-04-(01/07)
6 1.79 Donnerstag 2022-04-(01/07)
7 2.12 Freitag 2022-04-(08/14)
8 2.41 Samstag 2022-04-(08/14)
9 1.78 Sonntag 2022-04-(08/14)
10 3.19 Montag 2022-04-(08/14)
11 3.33 Dienstag 2022-04-(08/14)
12 2.88 Mittwoch 2022-04-(08/14)
13 2.98 Donnerstag 2022-04-(08/14)
14 3.01 Freitag 2022-04-(15/21)
15 3.04 Samstag 2022-04-(15/21)
16 2.72 Sonntag 2022-04-(15/21)
17 4.11 Montag 2022-04-(15/21)
18 3.90 Dienstag 2022-04-(15/21)
19 3.16 Mittwoch 2022-04-(15/21)
and so on, with ascending calendar weeks.
I performed a pivot table to generate a heatmap.
df_pivot = pd.pivot_table(df, values=['alpha1'], index=['week_day'], columns=['calendar_week'])
What I get is:
alpha1 \
calendar_week 2022-(04-29/05-05) 2022-(05-27/06-02) 2022-(07-29/08-04)
week_day
Dienstag 3.32 2.09 4.04
Donnerstag 3.27 2.21 4.65
Freitag 2.83 3.08 4.19
Mittwoch 3.22 3.14 4.97
Montag 2.83 2.86 4.28
Samstag 2.62 3.62 3.88
Sonntag 2.81 3.25 3.77
\
calendar_week 2022-(08-26/09-01) 2022-04-(01/07) 2022-04-(08/14)
week_day
Dienstag 2.92 3.58 3.33
Donnerstag 3.58 1.79 2.98
Freitag 3.96 2.49 2.12
Mittwoch 3.09 3.48 2.88
Montag 3.85 3.81 3.19
Samstag 3.10 1.32 2.41
Sonntag 3.39 2.70 1.78
As you see the sorting of the pivot table is messed up. I need the same sorting for the columns (calendar weeks) as in the original dataframe.
I have been looking all over but couldn't find how to achieve this.
Would be also very nice, if the sorting of the rows remains the same.
Any help will be greatly appreciated
UPDATE
I didn't paste all the data. It would have been too long
The calendar_week column consist of following elements
'2022-04-(01/07)',
'2022-04-(08/14)',
'2022-04-(15/21)',
'2022-04-(22/28)',
'2022-(04-29/05-05)',
'2022-05-(06/12)',
'2022-05-(13/19)',
'2022-05-(20/26)',
'2022-(05-27/06-02)',
'2022-06-(03/09)'
'2022-06-(10/16)'
'2022-06-(17/23)'
'2022-06-(24/30)'
'2022-07-(01/07)'
'2022-07-(08/14)'
'2022-07-(15/21)'
'2022-07-(22/28)'
'2022-(07-29/08-04)'
'2022-08-(05/11)'
etc....
Each occurs 7 times in df. It represents a calendar week.
The sorting is the natural time sorting.
After pivoting the dataframe, the sorting of the column get messed up. And I guess it's due to the 2 different types: 2022-(07-29/08-04) and 2022-07-(15/21).
Try running this:
df_pivot.sort_values(by = ['calendar_week'], axis = 1, ascending = True)
I got the following output. Is this what you wanted?
calendar_week
2022-04-(01/07)
2022-04-(08/14)
2022-04-(15/21)
week_day
Dienstag
3.58
3.33
3.90
Donnerstag
1.79
2.98
NaN
Freitag
2.49
2.12
3.01
Mittwoch
3.48
2.88
3.16
Montag
3.81
3.19
4.11
be sure to remove the NaN values using the fillna() function.
I hope that answers it. :)
You can use an ordered Categorical for your week days and sort the dates after pivoting with sort_index:
# define the desired order of the days
days = ['Montag', 'Dienstag', 'Mittwoch', 'Donnerstag',
'Freitag', 'Samstag', 'Sonntag']
df_pivot = (df
.assign(week_day=pd.Categorical(df['week_day'], categories=days,
ordered=True))
.pivot_table(values='alpha1', index='week_day',
columns='calendar_week')
.sort_index(axis=1)
)
output:
calendar_week 2022-04-(01/07) 2022-04-(08/14) 2022-04-(15/21)
week_day
Montag 3.81 3.19 4.11
Dienstag 3.58 3.33 3.90
Mittwoch 3.48 2.88 3.16
Donnerstag 1.79 2.98 NaN
Freitag 2.49 2.12 3.01
Samstag 1.32 2.41 3.04
Sonntag 2.70 1.78 2.72
I then have a function which look for a specific date (in this case, 2022-01-26):
def get_days(data, date):
df = pd.read_csv(data)
df = df[(df['date'] >= date) & (df['date'] <= date)]
get_trading_session_times(df)
Which returns:
v vw o c h l n date time
0 134730.0 3.6805 3.60 3.61 3.90 3.58 494 2022-01-26 09:00:00
1 72594.0 3.6324 3.60 3.62 3.70 3.57 376 2022-01-26 09:01:00
2 51828.0 3.6151 3.62 3.63 3.65 3.57 278 2022-01-26 09:02:00
3 40245.0 3.6343 3.63 3.65 3.65 3.62 191 2022-01-26 09:03:00
4 76428.0 3.6094 3.64 3.62 3.66 3.57 298 2022-01-26 09:04:00
.. ... ... ... ... ... ... ... ... ...
868 176.0 3.1300 3.13 3.13 3.13 3.13 2 2022-01-26 23:53:00
869 550.0 3.1200 3.12 3.12 3.12 3.12 3 2022-01-26 23:56:00
870 460.0 3.1211 3.12 3.12 3.12 3.12 3 2022-01-26 23:57:00
871 1175.0 3.1201 3.12 3.12 3.12 3.12 6 2022-01-26 23:58:00
872 559.0 3.1102 3.11 3.11 3.11 3.11 5 2022-01-26 23:59:00
[873 rows x 9 columns]
When I then try to look for only times between 09:00 and 09:30 like so:
def get_trading_session_times(df):
df = df['time'].between_time('09:00', '09:30')
print(df)
I get the following error:
Index must be DatetimeIndex when filtering dataframe
Full code:
import pandas as pd
data = 'data\BBIG.csv'
date = '2022-01-26'
def get_days(data, date):
df = pd.read_csv(data)
df = df[(df['date'] >= date) & (df['date'] <= date)]
get_trading_session_times(df)
def get_trading_session_times(df):
df = df['time'].between_time('09:00', '09:30')
print(df)
get_days(data, date)
What am I doing wrong?
between_time is only valid if your index is a DateTiimeIndex
As your string time is well formatted, you can use between to compare them because your values can be sorted in lexicographical order.
>>> df[df['time'].between('09:00', '09:30')]
v vw o c h l n date time
0 134730.0 3.6805 3.60 3.61 3.90 3.58 494 2022-01-26 09:00:00
1 72594.0 3.6324 3.60 3.62 3.70 3.57 376 2022-01-26 09:01:00
2 51828.0 3.6151 3.62 3.63 3.65 3.57 278 2022-01-26 09:02:00
3 40245.0 3.6343 3.63 3.65 3.65 3.62 191 2022-01-26 09:03:00
4 76428.0 3.6094 3.64 3.62 3.66 3.57 298 2022-01-26 09:04:00
Update
If your time column contains a time object:
from datetime import time
df['time'] = pd.to_datetime(df['time']).dt.time
out = df[df['time'].between(time(9, 0), time(9, 30))]
print(out)
# Output
v vw o c h l n date time
0 134730.0 3.6805 3.60 3.61 3.90 3.58 494 2022-01-26 09:00:00
1 72594.0 3.6324 3.60 3.62 3.70 3.57 376 2022-01-26 09:01:00
2 51828.0 3.6151 3.62 3.63 3.65 3.57 278 2022-01-26 09:02:00
3 40245.0 3.6343 3.63 3.65 3.65 3.62 191 2022-01-26 09:03:00
4 76428.0 3.6094 3.64 3.62 3.66 3.57 298 2022-01-26 09:04:00
I am doing a project where I read in the historical values for a given stock, I then want to filter out the days where the price has jumped +5% or -5% into a different dataframe.
But I am struggling with the transfer of the row.
import pandas_datareader as web
import pandas as pd
import datetime
start = datetime.datetime(2015, 9, 1)
end = datetime.datetime(2019, 11, 2)
df1 = pd.DataFrame()
df = web.DataReader("amd", 'yahoo', start, end)
df['Close'] = df['Close'].astype(float)
df['Open'] = df['Open'].astype(float)
for row in df:
df['perchange'] = ((df['Close']-df['Open'])/df['Open'])*100
df['perchange'] = df['perchange'].astype(float)
if df['perchange'] >= 5.0:
df1 += df
if ['perchange'] <= -5.0:
df1 += df
df.to_csv('amd_volume_price_history.csv')
df1.to_csv('amd_5_to_5.csv')
You can do this to create a new dataframe with
the rows where the percentage of changes is greater than 5% in absolute value. As you can see Series.between has been used to performance a boolean indexing:
not_significant=((df['Close']-df['Open'])/df['Open']).between(-0.05,0.05)
df_filtered=df[~not_significant]
print(df_filtered)
Output
High Low Open Close Volume Adj Close
Date
2015-09-11 2.140000 1.810000 1.880000 2.010000 31010300 2.010000
2015-09-14 2.000000 1.810000 2.000000 1.820000 16458500 1.820000
2015-10-19 2.010000 1.910000 1.910000 2.010000 10670800 2.010000
2015-10-23 2.210000 2.100000 2.100000 2.210000 9564200 2.210000
2015-11-03 2.290000 2.160000 2.160000 2.280000 8705800 2.280000
... ... ... ... ... ... ...
2019-06-06 31.980000 29.840000 29.870001 31.820000 131267800 31.820000
2019-07-31 32.299999 30.299999 32.080002 30.450001 119190000 30.450001
2019-08-08 34.270000 31.480000 31.530001 33.919998 167278800 33.919998
2019-08-12 34.650002 32.080002 34.160000 32.430000 106936000 32.430000
2019-08-23 31.830000 29.400000 31.299999 29.540001 83681100 29.540001
[123 rows x 6 columns]
if you really need perchange column you can create changing the code:
df['Perchange']=(df['Close']-df['Open'])/df['Open']*100
not_significant=(df['Perchange']).between(-5,5)
df_filtered=df[~not_significant]
print(df_filtered)
Also you can use DataFrame.pct_change:
df['Perchange']=df[['Open','Close']].pct_change(axis=1).Close*100
Output
High Low Open Close Volume Adj Close \
Date
2015-09-11 2.140000 1.810000 1.880000 2.010000 31010300 2.010000
2015-09-14 2.000000 1.810000 2.000000 1.820000 16458500 1.820000
2015-10-19 2.010000 1.910000 1.910000 2.010000 10670800 2.010000
2015-10-23 2.210000 2.100000 2.100000 2.210000 9564200 2.210000
2015-11-03 2.290000 2.160000 2.160000 2.280000 8705800 2.280000
... ... ... ... ... ... ...
2019-06-06 31.980000 29.840000 29.870001 31.820000 131267800 31.820000
2019-07-31 32.299999 30.299999 32.080002 30.450001 119190000 30.450001
2019-08-08 34.270000 31.480000 31.530001 33.919998 167278800 33.919998
2019-08-12 34.650002 32.080002 34.160000 32.430000 106936000 32.430000
2019-08-23 31.830000 29.400000 31.299999 29.540001 83681100 29.540001
Perchange
Date
2015-09-11 6.914893
2015-09-14 -8.999997
2015-10-19 5.235603
2015-10-23 5.238102
2015-11-03 5.555550
... ...
2019-06-06 6.528285
2019-07-31 -5.081050
2019-08-08 7.580074
2019-08-12 -5.064401
2019-08-23 -5.622998
[123 rows x 7 columns]
your code would look like this:
#Libraries
import pandas_datareader as web
import pandas as pd
import datetime
#Getting data
start = datetime.datetime(2015, 9, 1)
end = datetime.datetime(2019, 11, 2)
df = web.DataReader("amd", 'yahoo', start, end)
#Convertint to float to calculate and filtering
df['Close'] = df['Close'].astype(float)
df['Open'] = df['Open'].astype(float)
#Creating Perchange column.
df['Perchange']=(df['Close']-df['Open'])/df['Open']*100
#df['Perchange']=df[['Open','Close']].pct_change(axis=1).Close*100
#Filtering
not_significant=(df['Perchange']).between(-5,5)
df_filtered=df[~not_significant]
#Saving data.
df.to_csv('amd_volume_price_history.csv')
df_filtered.to_csv('amd_5_to_5.csv')
EDIT
df['Perchange']=(df['Close']-df['Open'])/df['Open']*100
significant=~(df['Perchange']).between(-5,5)
group_by_jump=significant.cumsum()
jump_and_4=group_by_jump.groupby(group_by_jump,sort=False).cumcount().le(4)&group_by_jump.ne(0)
df_filtered=df[jump_and_4]
print(df_filtered.head(50))
High Low Open Close Volume Adj Close Perchange
Date
2015-09-11 2.14 1.81 1.88 2.01 31010300 2.01 6.914893
2015-09-14 2.00 1.81 2.00 1.82 16458500 1.82 -8.999997
2015-09-15 1.87 1.81 1.84 1.86 6524400 1.86 1.086955
2015-09-16 1.90 1.85 1.87 1.89 4928300 1.89 1.069518
2015-09-17 1.94 1.87 1.90 1.89 5831600 1.89 -0.526315
2015-09-18 1.92 1.85 1.87 1.87 11814000 1.87 0.000000
2015-10-19 2.01 1.91 1.91 2.01 10670800 2.01 5.235603
2015-10-20 2.03 1.97 2.00 2.02 5584200 2.02 0.999999
2015-10-21 2.12 2.01 2.02 2.10 14944100 2.10 3.960392
2015-10-22 2.16 2.09 2.10 2.14 8208400 2.14 1.904772
2015-10-23 2.21 2.10 2.10 2.21 9564200 2.21 5.238102
2015-10-26 2.21 2.12 2.21 2.15 6313500 2.15 -2.714929
2015-10-27 2.16 2.10 2.12 2.15 5755600 2.15 1.415104
2015-10-28 2.20 2.12 2.14 2.18 6950600 2.18 1.869157
2015-10-29 2.18 2.11 2.15 2.13 4500400 2.13 -0.930232
2015-11-03 2.29 2.16 2.16 2.28 8705800 2.28 5.555550
2015-11-04 2.30 2.18 2.27 2.20 8205300 2.20 -3.083698
2015-11-05 2.24 2.17 2.21 2.20 4302200 2.20 -0.452488
2015-11-06 2.21 2.13 2.19 2.15 8997100 2.15 -1.826482
2015-11-09 2.18 2.10 2.15 2.11 6231200 2.11 -1.860474
2015-11-18 2.15 1.98 1.99 2.12 9384700 2.12 6.532657
2015-11-19 2.16 2.09 2.10 2.14 4704300 2.14 1.904772
2015-11-20 2.25 2.13 2.14 2.22 10727100 2.22 3.738314
2015-11-23 2.24 2.18 2.22 2.22 4863200 2.22 0.000000
2015-11-24 2.40 2.17 2.20 2.34 15859700 2.34 6.363630
2015-11-25 2.40 2.31 2.36 2.38 6914800 2.38 0.847467
2015-11-27 2.38 2.32 2.37 2.33 2606600 2.33 -1.687762
2015-11-30 2.37 2.25 2.34 2.36 9924400 2.36 0.854700
2015-12-01 2.37 2.31 2.36 2.34 5646400 2.34 -0.847457
2015-12-16 2.55 2.37 2.39 2.54 19543600 2.54 6.276144
2015-12-17 2.60 2.52 2.52 2.56 11374100 2.56 1.587300
2015-12-18 2.55 2.42 2.51 2.45 17988100 2.45 -2.390436
2015-12-21 2.53 2.43 2.47 2.53 6876600 2.53 2.429147
2015-12-22 2.78 2.54 2.55 2.77 24893200 2.77 8.627452
2015-12-23 2.94 2.75 2.76 2.83 30365300 2.83 2.536229
2015-12-24 3.00 2.86 2.88 2.92 11890900 2.92 1.388888
2015-12-28 3.02 2.86 2.91 3.00 16050500 3.00 3.092780
2015-12-29 3.06 2.97 3.04 3.00 15300900 3.00 -1.315788
2016-01-06 2.71 2.47 2.66 2.51 23759400 2.51 -5.639101
2016-01-07 2.48 2.26 2.43 2.28 22203500 2.28 -6.172843
2016-01-08 2.42 2.10 2.36 2.14 31822400 2.14 -9.322025
2016-01-11 2.36 2.12 2.16 2.34 19629300 2.34 8.333325
2016-01-12 2.46 2.28 2.40 2.39 17986100 2.39 -0.416666
2016-01-13 2.45 2.21 2.40 2.25 12749700 2.25 -6.250004
2016-01-14 2.35 2.21 2.29 2.21 15666600 2.21 -3.493447
2016-01-15 2.13 1.99 2.10 2.03 21199300 2.03 -3.333330
2016-01-19 2.11 1.90 2.08 1.95 18978900 1.95 -6.249994
2016-01-20 1.95 1.75 1.81 1.80 29243600 1.80 -0.552486
2016-01-21 2.18 1.81 1.82 2.09 26387900 2.09 14.835157
2016-01-22 2.17 1.98 2.11 2.02 16245500 2.02 -4.265399
try to integrate your code with these modifications:
1) you probably don't need any loop to calculate the new column:
df['perchange'] = ((df['Close']-df['Open'])/df['Open'])*100
df['perchange'] = df['perchange'].astype(float)
2) define an empty df
df1=pd.DataFrame([])
3) filter the old df with loc method (get used with its notation it is very useful) and append to the empty data frame, this will transfer the rows that verify the condition
df1=df1.append(df.loc[(df['perchange'] <= -5.0) | (df['perchange'] >= -5.0)])
print(df1)
hope it helps
I have a set of stock market data, sampled below.
I would like to like to work out the MAX ‘close’ price over each 5 day period.
symbol date open high low close volume
AAU 1-Jan-07 2.25 2.25 2.25 2.25 0
AAU 2-Jan-07 2.25 2.25 2.25 2.25 0
AAU 3-Jan-07 2.32 2.32 2.26 2.26 39800
AAU 4-Jan-07 2.29 2.35 2.27 2.32 114200
AAU 5-Jan-07 2.32 2.32 2.26 2.27 113600
AAU 8-Jan-07 2.27 2.35 2.1 2.33 84500
AAU 9-Jan-07 2.31 2.31 2.21 2.23 54200
AAU 10-Jan-07 2.24 2.3 2.2 2.3 29000
AAU 11-Jan-07 2.23 2.33 2.22 2.24 21400
AAU 12-Jan-07 2.25 2.33 2.25 2.33 45200
To do this I have added a new column to calculate the end date range (+5 days):
df[‘1w_date'] = df[‘date'].shift(-6)
The df then looks like this:
symbol date open high low close volume 5d_date
AAU 1-Jan-07 2.25 2.25 2.25 2.25 0 8-Jan-07
AAU 2-Jan-07 2.25 2.25 2.25 2.25 0 9-Jan-07
AAU 3-Jan-07 2.32 2.32 2.26 2.26 39800 10-Jan-07
AAU 4-Jan-07 2.29 2.35 2.27 2.32 114200 11-Jan-07
AAU 5-Jan-07 2.32 2.32 2.26 2.27 113600 12-Jan-07
AAU 8-Jan-07 2.27 2.35 2.1 2.33 84500 15-Jan-07
AAU 9-Jan-07 2.31 2.31 2.21 2.23 54200 16-Jan-07
AAU 10-Jan-07 2.24 2.3 2.2 2.3 29000 17-Jan-07
AAU 11-Jan-07 2.23 2.33 2.22 2.24 21400 18-Jan-07
AAU 12-Jan-07 2.25 2.33 2.25 2.33 45200 19-Jan-07
Next I set the date column as the df Index:
df = df.set_index(['date'])
Then I attempt to loop through each row using the ‘date’ as the start date and the ‘5d_date’ as the end date.
for i in df:
date_filter = df.loc[df[‘date’]:df[‘5d_date']]
df[‘min_value'] = min(date_filter['low'])
df[‘max_value'] = max(date_filter['high'])
Unfortunately I get a KeyError: ‘date’.
I have tried many different ways, but cannot figure out how to do this. Does anyone know how to fix this, or a better way of doing it?
Thanks.
After you set the index to date, you can use pd.DataFrame.rolling:
df.rolling('7d')['close'].mean()
Out[93]:
date
2007-01-01 2.250000
2007-01-02 2.250000
2007-01-03 2.253333
2007-01-04 2.270000
2007-01-05 2.270000
2007-01-08 2.286000
2007-01-09 2.282000
2007-01-10 2.290000
2007-01-11 2.274000
2007-01-12 2.286000
Name: close, dtype: float64
or, even without doing so,
df.rolling(5)['close'].mean()
Out[94]:
date
2007-01-01 NaN
2007-01-02 NaN
2007-01-03 NaN
2007-01-04 NaN
2007-01-05 2.270
2007-01-08 2.286
2007-01-09 2.282
2007-01-10 2.290
2007-01-11 2.274
2007-01-12 2.286
Name: close, dtype: float64
depending on whether you want a week (1), or five rows of data (2).
To have either of these at the start of the range instead of the end, just add .shift(-4) to the latter, and even to the former if you really do have exactly five days per week, every week.
I have a large file which contains information based on the number of processes and benchmark case used. All this information is followed one after the other within the same file.
--
# Benchmarking Allgather
# #processes = 8
# ( 3592 additional processes waiting in MPI_Barrier)
#----------------------------------------------------------------
#bytes #repetitions t_min[usec] t_max[usec] t_avg[usec]
0 1000 0.05 0.05 0.05
1 1000 1.77 2.07 1.97
2 1000 1.79 2.08 1.97
4 1000 1.79 2.07 1.98
8 1000 1.82 2.12 2.01
--
# Benchmarking Allgather
# #processes = 16
# ( 3584 additional processes waiting in MPI_Barrier)
#----------------------------------------------------------------
#bytes #repetitions t_min[usec] t_max[usec] t_avg[usec]
0 1000 0.05 0.05 0.05
1 1000 2.34 2.85 2.73
2 1000 2.36 2.87 2.74
4 1000 2.38 2.90 2.76
8 1000 2.42 2.95 2.79
In order to quickly plot the information I was planning to create a file per independent content, for instance, with the information given above I would create two files called "Allgather_8" and "Allgather_16" and the expected content of these files would be:
$cat Allgather_8
#bytes #repetitions t_min[usec] t_max[usec] t_avg[usec]
0 1000 0.05 0.05 0.05
1 1000 1.77 2.07 1.97
2 1000 1.79 2.08 1.97
4 1000 1.79 2.07 1.98
8 1000 1.82 2.12 2.01
$cat Allgather_16
#bytes #repetitions t_min[usec] t_max[usec] t_avg[usec]
0 1000 0.05 0.05 0.05
1 1000 2.34 2.85 2.73
2 1000 2.36 2.87 2.74
4 1000 2.38 2.90 2.76
8 1000 2.42 2.95 2.79
I could then plot this with gnuplot or matplotlib.
What I have tried so far:
I have been using grep and awk to extract the content, which works for independent sections but I don't know how to automate this.
Any ideas?
awk '
/Benchmarking/ { close(out); out = $NF }
/#processes/ { out = out "_" $NF }
/^[[:space:]]/ { print > out }
' file