I have a df that looks like the following:
TotalSpend Date
100 2001-04-26
230 2001-05-12
340 2001-06-16
610 2001-07-31
770 2001-08-31
I'm trying interpolate the data so I can see how much was spent during each month like so:
TotalSpend Date MonthlySpend
110 2001-04-30
310 2001-05-31 200
400 2001-06-30 90
610 2001-07-31 210
770 2001-08-31 160
I set the date column as the index and have tried to upsample the data (below) so that I have every day of the year and can then interpolate the missing values and select the month ends however this is proving troublesome.
resample = realdf.resample('d').mean()
Any help would be much appreciated.
Related
I have a df who looks like this:
Total Initial Follow Sched Supp Any
0 5525 3663 968 296 65 533
I transpose the df 'cause I have to add a column with the percentages based on column 'Total'
Now my df looks like this:
0
Total 5525
Initial 3663
Follow 968
Sched 296
Supp 65
Any 533
So, How can I add this percentage column?
The expected output looks like this
0 Percentage
Total 5525 100
Initial 3663 66.3
Follow 968 17.5
Sched 296 5.4
Supp 65 1.2
Any 533 9.6
Do you know how can I add this new column?
I'm working in jupyterlab with pandas and numpy
Multiple column 0 by scalar from Total with Series.div, then multiple by 100 by Series.mul and last round by Series.round:
df['Percentage'] = df[0].div(df.loc['Total', 0]).mul(100).round(1)
print (df)
0 Percentage
Total 5525 100.0
Initial 3663 66.3
Follow 968 17.5
Sched 296 5.4
Supp 65 1.2
Any 533 9.6
Consider below df:
In [1328]: df
Out[1328]:
b
a
Total 5525
Initial 3663
Follow 968
Sched 296
Supp 65
Any 533
In [1327]: df['Perc'] = round(df.b.div(df.loc['Total', 'b']) * 100, 1)
In [1330]: df
Out[1330]:
b Perc
a
Total 5525 100.0
Initial 3663 66.3
Follow 968 17.5
Sched 296 5.4
Supp 65 1.2
Any 533 9.6
I am trying to get difference between two date columns below script and data used in script, but I am getting same results for all three rows
df = pd.read_csv(r'Book1.csv',encoding='cp1252')
df
Out[36]:
Start End DifferenceinDays DifferenceinHrs
0 10/26/2013 12:43 12/15/2014 0:04 409 9816
1 2/3/2014 12:43 3/25/2015 0:04 412 9888
2 5/14/2014 12:43 7/3/2015 0:04 409 9816
I am expecting results as in column DifferenceinDays which is calculated in excel but in python getting same values for all three rows, Please refer to below code used, can anyone let me know how is to calculate difference between 2 date column, I am trying to get number of hours between two date columns.
df["Start"] = pd.to_datetime(df['Start'])
df["End"] = pd.to_datetime(df['End'])
df['hrs']=(df.End-df.Start)
df['hrs']
Out[38]:
0 414 days 11:21:00
1 414 days 11:21:00
2 414 days 11:21:00
Name: hrs, dtype: timedelta64[ns]
IIUC, np.timedelta64(1,'h')
Additionally, it looks like excel calculates the hours differently, unsure why.
import numpy as np
df['hrs'] = (df['End'] - df['Start']) / np.timedelta64(1,'h')
print(df)
Start End DifferenceinHrs hrs
0 2013-10-26 12:43:00 2014-12-15 00:04:00 9816 9947.35
1 2014-02-03 12:43:00 2015-03-25 00:04:00 9888 9947.35
2 2014-05-14 12:43:00 2015-07-03 00:04:00 9816 9947.35
I have a scenario where I would like to group my datasets by personally defined week indexes that are then averaged and aggregate the averages into a "Total" row. I am able to achieve the first half of my scenario, but when I try to append/insert a new "Total" row that sums these rows I am receiving error messages.
I attempted to create this row via two different methods:
Method 1:
week_index_avg_unit.loc['Total'] = week_index_avg_unit.sum()
TypeError: cannot append a non-category item to a CategoricalIndex
Method 2:
week_index_avg_unit.index.insert(['Total'], week_index_avg_unit.sum())
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
I have used the first approach in this scenario multiple times, but this is the first time where I'm cutting the data into multiple categories and clearly see where the CategoricalIndex type is the problem.
Here is the format of my data:
date organic ppc oa other content_partnership total \
0 2018-01-01 379 251 197 51 0 878
1 2018-01-02 880 527 405 217 0 2029
2 2018-01-03 859 589 403 323 0 2174
3 2018-01-04 835 533 409 335 0 2112
4 2018-01-05 760 449 355 272 0 1836
year_month day weekday weekday_name week_index
0 2018-01 1 0 Monday Week 1
1 2018-01 2 1 Tuesday Week 1
2 2018-01 3 2 Wednesday Week 1
3 2018-01 4 3 Thursday Week 1
4 2018-01 5 4 Friday Week 1
Here is the code:
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
historicals = pd.read_csv("2018-2019_plants.csv")
# Capture dates for additional date columns
date_col = pd.to_datetime(historicals['date'])
historicals['year_month'] = date_col.dt.strftime("%Y-%m")
historicals['day'] = date_col.dt.day
historicals['weekday'] = date_col.dt.dayofweek
historicals['weekday_name'] = date_col.dt.day_name()
# create week ranges segment (7 day range)
historicals['week_index'] = pd.cut(historicals['day'],[0,7,14,21,28,32], labels=['Week 1','Week 2','Week 3','Week 4','Week 5'])
# Week Index Average (Units)
week_index_avg_unit = historicals[df_monthly_average].groupby(['week_index']).mean().astype(int)
type(week_index_avg_unit.index)
pandas.core.indexes.category.CategoricalIndex
Here is the week_index_avg_unit table:
organic ppc oa other content_partnership total day weekday
week_index
Week 1 755 361 505 405 22 2027 4 3
Week 2 787 360 473 337 19 1959 11 3
Week 3 781 382 490 352 18 2006 18 3
...
pd.CategoricalIndex is a special animal. It is immutable, so to do the trick you may need to use something like pd.CategoricalIndex.set_categories to add a new category.
See pandas docs: https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.CategoricalIndex.html
For the following table
count_value
CPUCore Offline_RetentionAge
i7 183 4184
7 1981
30 471
i5 183 2327
7 831
30 250
Pentium 183 333
7 125
30 43
2 183 575
7 236
31 96
Is it possible to generate a seaborn countplot (or normal countplot) like the following (generated using sns.countplot(x='CPUCore', hue="Offline_BackupSchemaIncrementType", data=dfCombined_df))
Problem here is that I need to use the count_value as count, rather then really go and count the Offline_RetentionAge
I think you need seaborn.barplot:
sns.barplot(x="count_value", y="index", hue='Offline_RetentionAge', data=df.reset_index())
I would like to generate the following test data in my dataframe in a way similar to this:
df = pd.DataFrame(data=np.linspace(1800, 100, 400), index=pd.date_range(end='2015-07-02', periods=400), columns=['close'])
df
close
2014-05-29 1800.000000
2014-05-30 1795.739348
2014-05-31 1791.478697
2014-06-01 1787.218045
But using the following criteria:
intervals of 1 minute
increments of .25
prices moving up and down around 1800.00
maximum 2100.00, minimum 1700.00
parse_dates= "Timestamp"
Volume column rows have a range of min = 50 - max = 300
Day start 09:30 Day End 16:29:59
Please see desired output:
Open High Low Last Volume
Timestamp
2014-03-04 09:30:00 1783.50 1784.50 1783.50 1784.50 171
2014-03-04 09:31:00 1784.75 1785.75 1784.50 1785.25 28
2014-03-04 09:32:00 1785.00 1786.50 1785.00 1786.50 81
2014-03-04 09:33:00 1786.00
I have limited python experience and find the example for Numpy etc hard to follow as they look to be focused on academia. Is it possible to assist with this?