My data looks like: People <-- Events <--Activities. The parent is People, of which the only variable is the person_id. Events and Activities both have a time index, along with event_id and activity_id, both which have a few features.
Members of the 'People' entity visit places at all different times. I am trying to generate deep features for people. If people is something like [1,2,3], how do I pass cut off times that create deep features for something like (Person,cutofftime): [1,January2], [1, January3]
If I have only 3 People, it seems like I can't pass a cutoff_time dataframe that has 10 rows (for example, person 1 with 10 possible cutoff times). Trying this gives me the error "Duplicated rows in cutoff time dataframe", despite dropping duplicates from my cutoff_times dataframe.
Must I include time index in the People Entity? This would leave my parent entity with multiple people in the index, although they would have different time index. My instinct is that the people entity should not include any datetime column. I would like to give cut off times to the DFS function.
My cutoff_times df.head looks like this, and has multiple instances of some people_id:
+-------------------------------------------+
| person_id time label |
+-------------------------------------------+
| 0 f_GZSVLYU 2019-12-06 0.0 |
| 1 f_ATBJEQS 2019-12-06 1.0 |
| 2 f_GLFYVAY 2019-12-06 0.5 |
| 3 f_DIHPTPA 2019-12-06 0.5 |
| 4 f_GZSVLYU 2019-12-02 1.0 |
+-------------------------------------------+
The Parent People Entity is like this:
+-------------------+
| person_id |
+-------------------+
| 0 f_GZSVLYU |
| 1 f_ATBJEQS |
| 2 f_GLFYVAY |
| 3 f_DIHPTPA |
| 4 f_DVOYHRQ |
+-------------------+
How can I make featuretools understand what I'm trying to do?
'Duplicated rows in cutoff time dataframe.' I have explored my cutoff_times df and there are no duplicate rows. Person_id, times, and labels all have multiple occurrences each but no 2 rows are the same. Could these duplicates the error is referring to be somewhere else in the EntitySet?
The answer is one row of the cutoff_df had the same ID and time but with different labels. That's a problem.
Related
I do wonder how it is possible to make sliding windows in Pandas.
I have a dataframe with three columns.
Country | Number | DayOfTheYear
===================================
No | 50 | 0
No | 20 | 1
No | 37 | 2
I would love to see 14 day chunks for every country and day combination.
The country think can be ignored for the moment, since I can filter those manually in some way. But imagine there is only one country, is there a smart way to get some sort of summed up sliding window, resulting in something like the following?
Country | Sum | DatesOftheYear
===================================
No | 504 | 0-13
No | 207 | 1-14
No | 337 | 2-15
I would also accept if if they where disjunct, being only 0-13, 14-27, etc.
But I just cannot come along with Pandas. I know an old SQL solution, but is there anybody having a nice idea for Pandas?
If you want a rolling windows of your dataframe, you can simply use the .rolling function of pandas : https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
In your case : df["Number"].rolling(14).sum()
I'm trying to create a forecasting process using hierarchical time series. My problem is that I can't find a way to create a for loop that hierarchically extracts daily time series from a pandas dataframe grouping the sum of quantities by date. The resulting daily time series should be passed to a function inside the loop, and the results stored in some other object.
Dataset
The initial dataset is a table that represents the daily sales data of 3 hierarchical levels: city, shop, product. The initial table has this structure:
+============+============+============+============+==========+
| Id_Level_1 | Id_Level_2 | Id_Level_3 | Date | Quantity |
+============+============+============+============+==========+
| Rome | Shop1 | Prod1 | 01/01/2015 | 50 |
+------------+------------+------------+------------+----------+
| Rome | Shop1 | Prod1 | 02/01/2015 | 25 |
+------------+------------+------------+------------+----------+
| Rome | Shop1 | Prod1 | 03/01/2015 | 73 |
+------------+------------+------------+------------+----------+
| Rome | Shop1 | Prod1 | 04/01/2015 | 62 |
+------------+------------+------------+------------+----------+
| ... | ... | ... | ... | ... |
+------------+------------+------------+------------+----------+
| Milan | Shop3 | Prod9 | 31/12/2018 | 185 |
+------------+------------+------------+------------+----------+
| Milan | Shop3 | Prod9 | 31/12/2018 | 147 |
+------------+------------+------------+------------+----------+
| Milan | Shop3 | Prod9 | 31/12/2018 | 206 |
+------------+------------+------------+------------+----------+
Each City (Id_Level_1) has many Shops (Id_Level_2), and each one has some Products (Id_Level_3). Each shop has a different mix of products (maybe shop1 and shop3 have product7, which is not available in other shops). All data are daily and the measure of interest is the quantity.
Hierarchical Index (MultiIndex)
I need to create a tree structure (hierarchical structure) to extract a time series for each "node" of the structure. I call a "node" a cobination of the hierarchical keys, i.e. "Rome" and "Milan" are nodes of Level 1, while "Rome|Shop1" and "Milan|Shop9" are nodes of level 2. In particulare, I need this on level 3, because each product (Id_Level_3) has different sales in each shop of each city. Here is the strict hierarchy.
Nodes of level 3 are "Rome, Shop1, Prod1", "Rome, Shop1, Prod2", "Rome, Shop2, Prod1", and so on. The key of the nodes is logically the concatenation of the ids.
For each node, the time series is composed by two columns: Date and Quantity.
# MultiIndex dataframe
Liv_Labels = ['Id_Level_1', 'Id_Level_2', 'Id_Level_3', 'Date']
df.set_index(Liv_Labels, drop=False, inplace=True)
The I need to extract the aggregated time series in order but keeping the hierarchical nodes.
Level 0:
Level_0 = df.groupby(level=['Data'])['Qta'].sum()
Level 1:
# Node Level 1 "Rome"
Level_1['Rome'] = df.loc[idx[['Rome'],:,:]].groupby(level=['Data']).sum()
# Node Level 1 "Milan"
Level_1['Milan'] = df.loc[idx[['Milan'],:,:]].groupby(level=['Data']).sum()
Level 2:
# Node Level 2 "Rome, Shop1"
Level_2['Rome',] = df.loc[idx[['Rome'],['Shop1'],:]].groupby(level=['Data']).sum()
... repeat for each level 2 node ...
# Node Level 2 "Milan, Shop9"
Level_2['Milan'] = df.loc[idx[['Milan'],['Shop9'],:]].groupby(level=['Data']).sum()
Attempts
I already tried creating dictionaries and multiindex, but my problem is that I can't get a proper "node" use inside the loop. I can't even extract the unique level nodes keys, so I can't collect a specific node time series.
# Get level labels
Level_Labels = ['Id_Liv'+str(n) for n in range(1, Liv_Num+1)]+['Data']
# Initialize dictionary
TimeSeries = {}
# Get Level 0 time series
TimeSeries["Level_0"] = df.groupby(level=['Data'])['Qta'].sum()
# Get othe levels time series from 1 to Level_Num
for i in range(1, Liv_Num+1):
TimeSeries["Level_"+str(i)] = df.groupby(level=Level_Labels[0:i]+['Data'])['Qta'].sum()
Desired result
I would like a loop the cycles my dataset with these actions:
Creates a structure of all the unique node keys
Extracts the node time series grouped by Date and Quantity
Store the time series in a structure for later use
Thanks in advance for any suggestion! Best regards.
FR
I'm currently working on a switch dataset that I polled from an sql database where each port on the respective switch has a data frame which has a time series. So to access this time series information for each specific port I represented the switches by their IP addresses and the various number of ports on the switch, and to make sure I don't re-query what I already queried before I used the .unique() method to get unique queries of each.
I set my index to be the IP and Port indices and accessed the port information like so:
def yield_df(df):
for ip in df.index.get_level_values('ip').unique():
for port in df.loc[ip].index.get_level_values('port').unique():
yield df.loc[ip].loc[port]
Then I cycled the port data frames with a for loop like so:
for port_df in yield_df(adb_df):
I'm sure there are faster ways to carry out these procedures in pandas but I hope this helps you start solving your problem
After looking for answers and trying everything could not figure out a way out, so here it goes.
I have a list of *.txt files that I want to merge by column. I am 100% sure that they have the same structure, as follows
File1
date | time | model_name1
1850-01-16 | 12:00:00 | 0.10
File2
date | time | model_name2
1850-01-16 | 12:00:00 | 0.50
File3..... and so on
Note: the vertical bars are just for clarity here.
Now my output should look like this:
Output
date | time | model_name1 | model_name2
1850-01-16 | 12:00:00 | 0.10 | 0.50
With the following piece of code
out_list4 = os.listdir(out_directory)
df_list = [pd.read_table(out_path+os.fsdecode(file_x), sep='\s+') for file_x in out_list4]
df_merged = reduce(lambda left,right: ,
pd.merge(left,right,on=['date'], how='outer'), df_list)
pd.DataFrame.to_csv(df_merged, out_path+'merged.txt', sep='\t', index=False)
I manage the following output:
Output
date | time_x | model_name1 |time_y | model_name2
1850-01-16 | 12:00:00 | 0.10 |12:00:00| 0.50
As expected since I only have the key ""on=['date']"".
Now if I try to write time as second key as follows: ""on=['date','time']"", it crashes with the following error:
Key error:'time'
and a long list of tracebacks.
I tried placing left_on/righ_on in case "date" was being handled as index. No use. I know the problem does not lie on the files, the structure is right, it is the code. Any help will be much appreciated. And sorry for readibility on the
So, the problem was before. I had defined ""out_list4"" as a list before:
out_list4 = list()
and it was making a mess at the end. Each data element on the list should have size 1872 x 3, but at the end it was adding them altogether again making one last entry be 1872 x 12 and no 'time' header.
Changing the definition of ""out_list4"" to:
out_list4 = []
did the trick. The tip came from Combine a list of pandas dataframes to one pandas dataframe.
I have a table with events which are grouped by a uid. All rows have the columns uid, visit_num and event_num.
visit_num is an arbitrary counter that occasionally increases. event_num is the counter of interactions within the visit.
I want to merge these two counters into a single interaction counter that keeps increasing by 1 for each event and continues to increase when then next visit has started.
As I only look at the relative distance between events, it's fine if I don't start the counter at 1.
|uid |visit_num|event_num|interaction_num|
| 1 | 1 | 1 | 1 |
| 1 | 1 | 2 | 2 |
| 1 | 2 | 1 | 3 |
| 1 | 2 | 2 | 4 |
| 2 | 1 | 1 | 500 |
| 2 | 2 | 1 | 501 |
| 2 | 2 | 2 | 502 |
I can achieve this by repartitioning the data and using the monotonically_increasing_id like this:
df.repartition("uid")\
.sort("visit_num", "event_num")\
.withColumn("iid", fn.monotonically_increasing_id())
However the documentation states:
The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive. The current implementation puts the partition ID in the upper 31 bits, and the record number within each partition in the lower 33 bits. The assumption is that the data frame has less than 1 billion partitions, and each partition has less than 8 billion records.
As the id seems to be monotonically increasing by partition this seems fine. However:
I am close to reaching the 1 billion partition/uid threshold.
I don't want to rely on the current implementation not changing.
Is there a way I can start each uid with 1 as the first interaction num?
Edit
After testing this some more, I notice that some of the users don't seem to have consecutive iid values using the approach described above.
Edit 2: Windowing
Unfortunately there are some (rare) cases where more thanone row has the samevisit_numandevent_num`. I've tried using the windowing function as below, but due to this assigning the same rank to two identical columns, this is not really an option.
iid_window = Window.partitionBy("uid").orderBy("visit_num", "event_num")
df_sample_iid=df_sample.withColumn("iid", fn.rank().over(iid_window))
The best solution is the Windowing function with rank, as suggested by Jacek Laskowski.
iid_window = Window.partitionBy("uid").orderBy("visit_num", "event_num")
df_sample_iid=df_sample.withColumn("iid", fn.rank().over(iid_window))
In my specific case some more data cleaning was required but generally, this should work.
Slightly wordy title but here goes
I have a grid in excel which includes 3 columns (media spend, marginal revenue returns & media channel invested in) and I want to create the column below called desired cumulative spend
The reason the grid is structured in this way it does is that it represents an optimised spend laydown ordered by how much of each media channel's budget should be invested in until the marginal returns diminish such that it should be substituted for another media channel.
It is possible that this substitution can then be reversed back to the original channel if the new channel has a sharply diminishing curve, such that all marginal benefit associated to the new channel diminishes and the total spend level still means it is mathematically sensible to switch back to the original curve (maybe it has a lower base level but reduces less sharply). It is also possible that at the point in which the marginal benefit associated to the new channel diminishes, the best next step is to invest in a third channel.
The desired new spend column has two elements to it
it is a simple accumulation of spend from row to row when the
media channel is constant from row to row
it is a slightly more tricky accumulation of spend when the media
channel changes - then it needs to be able to reference back to the
last spend level associated to the channel which has been
substituted in. For row 4, the logic I am struggling with would need
to the running total from row 3 plus the new spend level associated
to row 4 minus the spend level the last time this channel was used
(row 2)
|spend | mar return | media | desired cumulative spend |
|------ |----------- |-------| ----------------------------------------- |
1 | £580 | 128 | chan1 | 580 |
2 | £620 | 121 | chan1 | 580+(620-580) |
3 | £900 | 115.8 | chan2 | 580+(620-580)+900 |
4 | £660 | 115.1 | chan1 | 580+(620-580)+900+(660-620) |
5 | £920 | 114 | chan2 | 580+(620-580)+900+(660-620)+(920-900) |
6 | £940 | 112 | chan2 | 580+(620-580)+900+(660-620)+(920-900)+(940-920) |
If my comment is the correct sugestion, then something like this should do it (£580 is at A2, so the first output is D2):
D2 =A2
D3 =D2+A3-IF(COUNTIF($C$2:C2,C3),INDEX(A:A,MAX(IF($C$2:C2=C3,ROW($A$2:A2)))))
D3 contains an array formula and must be confirmed with ctrl+shift+enter.
Now you can simply copy down from D3.