I have a pandas dataframe something like the below:
Total Yr_to_Use First_Year_Del Del_rate 2019 2020 2021 2022 2023 etc
ref1 100 2020 5 10 0 0 0 0 0
ref2 20 2028 2 5 0 0 0 0 0
ref3 30 2021 7 16 0 0 0 0 0
ref4 40 2025 9 18 0 0 0 0 0
ref5 10 2022 4 30 0 0 0 0 0
The 'Total' column shows how many of a product needs to be delivered.
'First_yr_Del' tells you how many will be delivered in the first year. After this the delivery rate reverts to 'Del_rate' - a flat rate that can be applied each year until all products are delivered.
The 'Year to Use' column tells you the first year column to begin delivery from.
EXAMPLE: Ref1 has 100 to deliver. It will start delivering in 2020 and will deliver 5 in the first year, and 10 each year after that until all 100 are accounted for.
Any ideas how to go about this?
I thought i might use something like the below to reference which columns to use in turn, but i'm not even sure if that's helpful or not as it will depend on the solution (in the proper version, base_date.year is defined as the first column in the table - 2019):
start_index_for_slice = df.columns.get_loc(base_date.year)
end_index_for_slice = start_index_for_slice+no_yrs_to_project
df.columns[start_index_for_slice:end_index_for_slice]
I'm pretty new to python and aren't sure if i'm getting ahead of myself a bit...
The way i would think to go about it would be to use a for loop, or something using iterrows, but other posts seem to say this is a bad idea and i should be using vectorisation, cython or lambdas. Of those 3 i've only managed a very simple lambda so far. The others are a bit of a mystery to me since the solution seems to suggest doing one action after another until complete.
Any and all help appreciated!
Thanks
EDIT: Example expected output below (I edited some of the dates so you can better see the logic):
Total Yr_to_Use First_Year_Del Del_rate 2019 2020 2021 2022 2023etc
ref1 100 2020 5 10 0 5 10 10 10
ref2 20 2021 2 5 0 0 2 5 5
ref3 30 2021 7 16 0 0 7 16 7
ref4 40 2019 9 18 9 18 13 0 0
ref5 10 2020 4 30 0 4 6 0 0
Here's another option, which separates the calculation of the rates/years matrix and appends it to the input df later on. Still does looping in the script itself (not "externalized" to some numpy / pandas function). Should be fine for 5k rows I'd guesstimate.
import pandas as pd
import numpy as np
# def gen_df1():
# create the inital df without years/rates
df = pd.DataFrame({'Total': [100, 20, 30, 40, 10],
'Yr_to_Use': [2020, 2021, 2021, 2019, 2020],
'First_Year_Del': [5, 2, 7, 9, 10],
'Del_rate': [10, 5, 16, 18, 30]})
# get number of rates + remainder
n, r = np.divmod((df['Total']-df['First_Year_Del']), df['Del_rate'])
# get the year of the last rate considering all rows
max_year = np.max(n + r.astype(np.bool) + df['Yr_to_Use'])
# get the offsets for the start of delivery, year zero is 2019
offset = df['Yr_to_Use'] - 2019
# subtracting the year zero lets you use this as an index...
# get a year index; this determines the the columns that will be created
yrs = np.arange(2019, max_year+1)
# prepare a n*m array to hold the rates for all years, initalize with all zero
out = np.zeros((df['Total'].shape[0], yrs.shape[0]))
# n: number of rows of the df, m: number of years where rates will have to be payed
# calculate the rates for each year and insert them into the output array
for i in range(df['Total'].shape[0]):
# concatenate: year of the first rate, all yearly rates, a final rate if there was a remainder
if r[i]: # if rest is not zero, append it as well
rates = np.concatenate([[df['First_Year_Del'][i]], n[i]*[df['Del_rate'][i]], [r[i]]])
else: # rest is zero, skip it
rates = np.concatenate([[df['First_Year_Del'][i]], n[i]*[df['Del_rate'][i]]])
# insert the rates at the apropriate location of the output array:
out[i, offset[i]:offset[i]+rates.shape[0]] = rates
# add the years/rates matrix to the original df
df = pd.concat([df, pd.DataFrame(out, columns=yrs.astype(str))], axis=1, sort=False)
You can accomplish this using two user-defined function and apply method
import pandas as pd
import numpy as np
df = pd.DataFrame(data={'id': ['ref1','ref2','ref3','ref4','ref5'],
'Total': [100, 20, 30, 40, 10],
'Yr_to_Use': [2020, 2028, 2021, 2025, 2022],
'First_Year_Del': [5,2,7,9,4],
'Del_rate':[10,5,16,18,30]})
def f(r):
'''
Computes values per year and respective year
'''
n = (r['Total'] - r['First_Year_Del'])//r['Del_rate']
leftover = (r['Total'] - r['First_Year_Del'])%r['Del_rate']
r['values'] = [r['First_Year_Del']] + [r['Del_rate'] for _ in range(n)] + [leftover]
r['years'] = np.arange(r['Yr_to_Use'], r['Yr_to_Use'] + len(r['values']))
return r
df = df.apply(f, axis=1)
def get_year_range(r):
'''
Computes min and max year for each row
'''
r['y_min'] = min(r['years'])
r['y_max'] = max(r['years'])
return r
df = df.apply(get_year_range, axis=1)
y_min = df['y_min'].min()
y_max = df['y_max'].max()
#Initialize each year value to zero
for year in range(y_min, y_max+1):
df[year] = 0
def expand(r):
'''
Update value for each year
'''
for v, y in zip(r['values'], r['years']):
r[y] = v
return r
# Apply and drop temporary columns
df = df.apply(expand, axis=1).drop(['values', 'years', 'y_min', 'y_max'], axis=1)
Related
I have a pandas DataFrame with date as the index and a column, 'spendings'. I intend to get the rolling max() of the 'spendings' column for the trailing 1 calendar month (not 30 days or 4 weeks).
I tried to capture a snippet with custom data for addressing the problem, below (borrowed from Pandas monthly rolling operation):
import pandas as pd
from io import StringIO
data = StringIO(
"""\
date spendings
20210325 15
20210405 20
20210415 10
20210425 40
20210505 3
20210515 2
20210525 2
20210527 1
"""
)
df = pd.read_csv(data,sep="\s+", parse_dates=True)
df.index = pd.to_datetime(df.date, format='%Y%m%d')
del(df['date'])
Now, to create a column 'max' to hold rolling last 1 calendar month's max() val, I use:
df['max'] = df.loc[(df.index - pd.tseries.offsets.DateOffset(months=1)):df.index, 'spendings'].max()
This raises an exception like:
TypeError: cannot do slice indexing on DatetimeIndex with these indexers [DatetimeIndex(['2021-02-25', '2021-03-05', '2021-03-15', '2021-03-25',
'2021-04-05', '2021-04-15', '2021-04-25'],
dtype='datetime64[ns]', name='date', freq=None)] of type DatetimeIndex
However, if I manually access a random month window like below, it works without exception:
>>> df['2021-04-16':'2021-05-15']
spendings
date
2021-04-25 40
2021-05-05 3
2021-05-15 2
(I could have followed the method using list comprehension here: https://stackoverflow.com/a/47199274/235415, but I would like to use panda's vectorized method. I have many DataFrames and each is very large - using list comprehension is very slow here).
Q: How to get the vectorized method of performing rolling 1 calendar month's max()?
The expected o/p, ie primarily the 'max' column (holding the max value of 'spendings' for last 1 calendar month) will be something like this:
>>> df
spendings max
date
2021-03-25 15 15
2021-04-05 20 20
2021-04-15 10 20
2021-04-25 40 40
2021-05-05 3 40
2021-05-15 2 40
2021-05-25 2 40
2021-05-27 1 3
The answer will be
[df.loc[x- pd.tseries.offsets.DateOffset(months=1):x, 'spendings'].max() for x in df.index]
Out[53]: [15, 20, 20, 40, 40, 40, 40, 3]
I have a df over 1M rows similar to this
ID Date Amount
x May 1 10
y May 2 20
z May 4 30
x May 1 40
y May 1 50
z May 2 60
x May 1 70
y May 5 80
a May 6 90
b May 8 100
x May 10 110
I have to sort the data based on the date and then create new dataframes depending on the times the value is present in Amount column. So if x has made purchase 3 time then I need it in 3 different dataframes. first_purchase dataframe would have every ID that has purchased even once irrespective of date or amount.
If an ID purchases 3 times, I need that ID to be in first purchase then second and then 3rd with Date and Amount.
Doing it manually is easy with:-
df = df.sort_values('Date')
first_purchase = df.drop_duplicates('ID')
after_1stpurchase = df[~df.index.isin(first_purchase.index)]
second data frame would be created with:-
after_1stpurchase = after_1stpurchase.sort_values('Date')
second_purchase = after_1stpurchase.drop_duplicates('ID')
after_2ndpurchase = after_1stpurchase[~after_1stpurchase.index.isin(second_purchase.index)]
How do I create the loop to provide me with each dataframes?
IIUC, I was able to achieve what you wanted.
import pandas as pd
import numpy as np
# source data for the dataframe
data = {
"ID":["x","y","z","x","y","z","x","y","a","b","x"],
"Date":["May 01","May 02","May 04","May 01","May 01","May 02","May 01","May 05","May 06","May 08","May 10"],
"Amount":[10,20,30,40,50,60,70,80,90,100,110]
}
df = pd.DataFrame(data)
# convert the Date column to datetime and still maintain the format like "May 01"
df['Date'] = pd.to_datetime(df['Date'], format='%b %d').dt.strftime('%b %d')
# sort the values on ID and Date
df.sort_values(by=['ID', 'Date'], inplace=True)
df.reset_index(inplace=True, drop=True)
print(df)
Original Dataframe:
Amount Date ID
0 90 May 06 a
1 100 May 08 b
2 10 May 01 x
3 40 May 01 x
4 70 May 01 x
5 110 May 10 x
6 50 May 01 y
7 20 May 02 y
8 80 May 05 y
9 60 May 02 z
10 30 May 04 z
.
# create a list of unique ids
list_id = sorted(list(set(df['ID'])))
# create an empty list that would contain dataframes
df_list = []
# count of iterations that must be seperated out
# for example if we want to record 3 entries for
# each id, the iter would be 3. This will create
# three new dataframes that will hold transactions
# respectively.
iter = 3
for i in range(iter):
df_list.append(pd.DataFrame())
for val in list_id:
tmp_df = df.loc[df['ID'] == val].reset_index(drop=True)
# consider only the top iter(=3) values to be distributed
counter = np.minimum(tmp_df.shape[0], iter)
for idx in range(counter):
df_list[idx] = df_list[idx].append(tmp_df.loc[tmp_df.index == idx])
for df in df_list:
df.reset_index(drop=True, inplace=True)
print(df)
Transaction #1:
Amount Date ID
0 90 May 06 a
1 100 May 08 b
2 10 May 01 x
3 50 May 01 y
4 60 May 02 z
Transaction #2:
Amount Date ID
0 40 May 01 x
1 20 May 02 y
2 30 May 04 z
Transaction #3:
Amount Date ID
0 70 May 01 x
1 80 May 05 y
Note that in your data, there are four transactions for 'x'. If lets say you wanted to track the 4th iterative transaction as well. All you need to do is change the value if 'iter' to 4 and you will get the fourth dataframe as well with the following value:
Amount Date ID
0 110 May 10 x
I have a dataframe with phone calls, some of them are of zero duration. I want to replace them with int values ranging from 0 to 7, but every my attempt leads to errors or data loss.
I wrote function:
def calls_new(dur):
dur = random.randint(0,7)
return dur
and I tried to use it like this (one of these lines):
df_calls['duration'] = df_calls['duration'].apply(lambda row: x = random.randint(0,7) if x == 0 )
df_calls['duration'] = df_calls['duration'].where(df_calls['duration'] == 0, df_calls.apply(calls_new))
df_calls['duration'] = df_calls[df_calls['duration']==0].apply(calls_new)
Use .loc to set the values only where duration is 0. You can generate all of the random numbers and set everything at once. If you want 7, the end of randint needs to be 8 as the docs indicate high is one above the largest integer to be drawn.
import pandas as pd
import numpy as np
np.random.seed(42)
df = pd.DataFrame({'duration': [0,10,20,0,15,0,0,211]})
m = df['duration'].eq(0)
df.loc[m, 'duration'] = np.random.randint(0, 8, m.sum())
# |
# Need this many numbers
print(df)
duration
0 4
1 10
2 20
3 7
4 15
5 6
6 2
7 211
I have 4 dataframes with weekly sales values for a year for 4 products. Some of the initial rows are 0 as no sales. there are some other 0 values as well in between the weeks.
I want to remove those initial 0 values, keeping the in between 0s.
For example
Week Sales(prod 1)
1 0
2 0
3 100
4 120
5 55
6 0
7 60.
Week Sales(prod 2)
1 0
2 0
3 0
4 120
5 0
6 30
7 60.
I want to remove row 1,2 from 1st table and 1,2,3 frm 2nd.
Few Assumption based on your example dataframe:
DataFrame is created using pandas
week always start with 1
will remove all the starting weeks only which are having 0 sales
Solution:
Python libraries Required
- pandas, more_itertools
Example DataFrame (df):
Week Sales
1 0
2 0
3 0
4 120
5 0
6 30
7 60
Python Code:
import pandas as pd
import more_itertools as mit
filter_col = 'Sales'
filter_val = 0
##function which returns the index to be removed
def return_initial_week_index_with_zero_sales(df,filter_col,filter_val):
index_wzs = [False]
if df[filter_col].iloc[1]==filter_val:
index_list = df[df[filter_col]==filter_val].index.tolist()
index_wzs = [list(group) for group in mit.consecutive_groups(index_list)]
else:
pass
return index_wzs[0]
##calling above function and removing index from the dataframe
df = df.set_index('Week')
weeks_to_be_removed = return_initial_week_index_with_zero_sales(df,filter_col,filter_val)
if weeks_to_be_removed:
print('Initial weeks with 0 sales are {}'.format(weeks_to_be_removed))
df = df.drop(index=weeks_to_be_removed)
else:
print('No initial week has 0 sales')
df.reset_index(inplace=True)
Result:df
Week Sales
4 120
5 55
6 0
7 60
I hope it helps, you can modify the function as per your requirement.
I would like to create a pandas Series that indicates whether a certain date - which is supposed to be the index of the Series - is a third friday in a month.
My idea is to create the Series first with zeroes as values and then changing those zeroes to ones where the index is a third friday in a month. This is my approach which seems to work.
import pandas as pd
import numpy as np
# 8 years of data
dates = pd.date_range("2010-01-01","2017-12-31")
# create series filled with zeroes over those 8 years
mySeries = pd.Series(np.zeros(len(dates)),index=dates)
# iterate over series and change value to 1 if index is a third friday in a month
for index,value in mySeries.iteritems():
if index.weekday() == 4 and 14 < index.day < 22:
mySeries[index] = 1
# if the sum of the series is 96 (1 third friday per month * 12 months * 8 years) then the solution should be correct
print(sum(mySeries))
I would be interested to see other and easier solutions as well of course.
Use non loop faster solution with weekday and day, convert boolean mask to int with Series contructor:
dates = pd.date_range("2010-01-01","2017-12-31")
days = dates.day
s1 = pd.Series(((dates.weekday == 4) & (days > 14) & (days < 22)).astype(int), index=dates)
print (s1.sum())
96
print (s1.head())
2010-01-01 0
2010-01-02 0
2010-01-03 0
2010-01-04 0
2010-01-05 0
Freq: D, dtype: int32
Timings:
In [260]: %%timeit
...: mySeries = pd.Series(np.zeros(len(dates)),index=dates)
...:
...: # iterate over series and change value to 1 if index is a third friday in a month
...: for index,value in mySeries.iteritems():
...: if index.weekday() == 4 and 14 < index.day < 22:
...: mySeries[index] = 1
...:
The slowest run took 5.18 times longer than the fastest. This could mean that an intermediate result is being cached.
100 loops, best of 3: 2.68 ms per loop
Compiler time: 0.31 s
In [261]: %%timeit
...: days = dates.day
...: s1 = pd.Series(((dates.weekday == 4) & (days > 14) & (days < 22)).astype(int), index=dates)
...:
1000 loops, best of 3: 603 µs per loop