I have a column in my dataframe that goes indexwise from 900 to 1200.
Now I want to extract that index values automatically. Does anyone know how I could do that? I either would like to have the difference (1200-900) or the range [900,1200].
IIUC use:
out = (df.index.min(), df.index.max())
dif = df.index.max() - df.index.min()
#1200 missing
r = range(df.index.min(), df.index.max())
#include 1200
r1 = range(df.index.min(), df.index.max() + 1)
I have this dataframe -
data = [(0,1,1,201505,3),
(1,1,1,201506,5),
(2,1,1,201507,7),
(3,1,1,201508,2),
(4,2,2,201750,3),
(5,2,2,201751,0),
(6,2,2,201752,1),
(7,2,2,201753,1)
]
cols = ['id','item','store','week','sales']
data_df = spark.createDataFrame(data=data,schema=cols)
display(data_df)
What I want it this -
data_new = [(0,1,1,201505,3,0),
(1,1,1,201506,5,0),
(2,1,1,201507,7,0),
(3,1,1,201508,2,0),
(4,1,1,201509,0,0),
(5,1,1,201510,0,0),
(6,1,1,201511,0,0),
(7,1,1,201512,0,0),
(8,2,2,201750,3,0),
(9,2,2,201751,0,0),
(10,2,2,201752,1,0),
(11,2,2,201753,1,0),
(12,2,2,201801,0,0),
(13,2,2,201802,0,0),
(14,2,2,201803,0,0),
(15,2,2,201804,0,0)]
cols_new = ['id','item','store','week','sales','flag',]
data_df_new = spark.createDataFrame(data=data_new,schema=cols_new)
display(data_df_new)
So basically, I want 8 (this can also be 6 or 10) weeks of data for each item-store groupby combination. Wherever the 52/53 weeks for the year ends, I need the weeks for the next year, as I have mentioned in the sample. I need this in PySpark, thanks in advance!
I think I have a problem with time calculation.
I want to run this code on a DataFrame of 320 000 lines, 6 columns:
index_data = data["clubid"].index.tolist()
for i in index_data:
for j in index_data:
if data["clubid"][i] == data["clubid"][j]:
if data["win_bool"][i] == 1:
if (data["startdate"][i] >= data["startdate"][j]) & (
data["win_bool"][j] == 1
):
NW_tot[i] += 1
else:
if (data["startdate"][i] >= data["startdate"][j]) & (
data["win_bool"][j] == 0
):
NL_tot[i] += 1
The objective is to determine the number of wins and the number of losses from a given match taking into account the previous match, this for every clubid.
The problem is, I don't get an error, but I never obtain any results either.
When I tried with a smaller DataFrame ( data[0:1000] ) I got a result in 13 seconds. This is why I think it's a time calculation problem.
I also tried to first use a groupby("clubid"), then do my for loop into every group but I drowned myself.
Something else that bothers me, I have at least 2 lines with the exact same date/hour, because I have at least two identical dates for 1 match. Because of this I can't put the date in index.
Could you help me with these issues, please?
As I pointed out in the comment above, I think you can simply sum the vector of win_bool by group. If the dates are sorted this should be equivalent to your loop, correct?
import pandas as pd
dat = pd.DataFrame({
"win_bool":[0,0,1,0,1,1,1,0,1,1,1,1,1,1,0],
"clubid": [1,1,1,1,1,1,1,2,2,2,2,2,2,2,2],
"date" : [1,2,1,2,3,4,5,1,2,1,2,3,4,5,6],
"othercol":["a","b","b","b","b","b","b","b","b","b","b","b","b","b","b"]
})
temp = dat[["clubid", "win_bool"]].groupby("clubid")
NW_tot = temp.sum()
NL_tot = temp.count()
NL_tot = NL_tot["win_bool"] - NW_tot["win_bool"]
If you have duplicate dates that inflate the counts, you could first drop duplicates by dates (within groups):
# drop duplicate dates
temp = dat.drop_duplicates(["clubid", "date"])[["clubid", "win_bool"]].groupby("clubid")
I have a large dataset pertaining customer churn, where every customer has an unique identifier (encoded key). The dataset is a timeseries, where every customer has one row for every month they have been a customer, so both the date and customer-identifier column naturally contains duplicates. What I am trying to do is to add a new column (called 'churn') and set the column to 0 or 1 based on if it is that specific customer's last month as a customer or not.
I have tried numerous methods to do this, but each and every one fails, either do to tracebacks or they just don't work as intended. It should be noted that I am very new to both python and pandas, so please explain things like I'm five (lol).
I have tried using pandas groupby to group rows by the unique customer keys, and then checking conditions:
df2 = df2.groupby('customerid').assign(churn = [1 if date==max(date) else 0 for date in df2['date']])
which gives tracebacks because dataframegroupby object has no attribute assign.
I have also tried the following:
df2.sort_values(['date']).groupby('customerid').loc[df['date'] == max('date'), 'churn'] = 1
df2.sort_values(['date']).groupby('customerid').loc[df['date'] != max('date'), 'churn'] = 0
which gives a similar traceback, but due to the attribute loc
I have also tried using numpy methods, like the following:
df2['churn'] = df2.groupby(['customerid']).np.where(df2['date'] == max('date'), 1, 0)
which again gives tracebacks due to the dataframegroupby
and:
df2['churn'] = np.where((df2['date']==df2['date'].max()), 1, df2['churn'])
which does not give tracebacks, but does not work as intended, i.e. it applies 1 to the churn column for the max date for all rows, instead of the max date for the specific customerid - which in retrospect is completely understandable since customerid is not specified anywhere.
Any help/tips would be appreciated!
IIUC use GroupBy.transform with max for return maximal values per groups and compare with date column, last set 1,0 values by mask:
mask = df2['date'].eq(df2.groupby('customerid')['date'].transform('max'))
df2['churn'] = np.where(mask, 1, 0)
df2['churn'] = mask.astype(int)
I am trying to calculate a portfolio's daily total price, by multiplying weights of each asset with the daily price of the assets.
Currently I have a DataFrame tw which is all zeros except for the dates that I want to re-balance, which holds my assets weights. What I would like to do is for each month, populate the zeros with the weights I am trying to re-balance with, till the next re-balancing date, and so on and so forth.
My code:
df_of_weights = tw.loc[dates_to_rebalance[13]:]
temp_date = dates_to_rebalance[13]
counter = 0
for date in df_of_weights.index:
if date.year == temp_date.year and date.month == temp_date.month:
if date.day == temp_date.day:
pass
else:
df_of_weights.loc[date] = df_of_weights.loc[temp_date].values
counter += 1
temp_date = dates_to_rebalance[13+counter]
I understand that if you slice your DataFrame and assign it to a variable (df_of_weights), changing the values of said variable would not affect the original DataFrame. However, the values in tw changed. Have been searching for an answer online for a while now and am really confused.
You should use copy in order to fix the problem such that:
df_of_weights = tw.loc[dates_to_rebalance[13]:].copy()
The problem is slicing provides view instead of copy. The issue is still open.
https://github.com/pandas-dev/pandas/issues/15631