I am relatively new to this field and am working with a data set to find meaningful insights into customer behavior. My dataset looks like:
customerId week first_trip_week rides
0 156 44 36 2
1 164 44 38 6
2 224 42 36 5
3 224 43 36 4
4 224 44 36 5
What I want to do is create new columns week 44,week 43, week 42 and get the values in the "ride" column to be filled into the rows for the respective customer id. This is in the hope that I can eventually also make the customerId my index and can get denominations for different weeks. Help would be greatly appreciated!
Thank you!!
If I'm understanding you correctly, you want to create new columns in the same dataframe for weeks 44, 43, and 42 with the correct values for each customerId and NaN for those that don't have it. If your original dataframe has all the user data, I would first filter for dataframes that have the correct week number
week42DF = dataset.loc[dataset['week']==42,['customerId','rides']].rename(columns={'rides':'week42Rides'})
getting only the rides and customerId and renaming the former here to make things a little easier for us. Then left join the old dataframe and the new one on customerId
dataset = pd.merge(dataset,week42DF,how='left',on='customerId')
The users that are missing from week42DF will have NaN in the week42rides column in the merged dataset which you can then use the .fillna(0) method to replace with zeros. Do this for each week you require.
See Pandas' documentation on merge and the more general concatenate for more info.
Related
I have a pandas DataFrame with multiple measurements per day (for example hourly measurements, but that is not necessarily the case), but I want to keep only the hour for which a certain column is the daily minimum.
My one day in my data frame looks somewhat like this
DATE Value Distance
17 1979-1-2T00:00:00.0 15.5669870447436 34.87
18 1979-1-2T01:00:00.0 81.6306803714536 31.342
19 1979-1-2T02:00:00.0 83.1854759740486 33.264
20 1979-1-2T03:00:00.0 23.8659679630303 32.34
21 1979-1-2T04:00:00.0 63.2755504429306 31.973
22 1979-1-2T05:00:00.0 91.2129044773733 34.091
23 1979-1-2T06:00:00.0 76.493130052689 36.837
24 1979-1-2T07:00:00.0 63.5443183375785 34.383
25 1979-1-2T08:00:00.0 40.9255407683688 35.275
26 1979-1-2T09:00:00.0 54.5583051827551 32.152
27 1979-1-2T10:00:00.0 26.2690011881422 35.104
28 1979-1-2T11:00:00.0 71.3059740399097 37.28
29 1979-1-2T12:00:00.0 54.0111262724049 38.963
30 1979-1-2T13:00:00.0 91.3518048568241 36.696
31 1979-1-2T14:00:00.0 81.7651763485069 34.832
32 1979-1-2T15:00:00.0 90.5695814525067 35.473
33 1979-1-2T16:00:00.0 88.4550315358515 30.998
34 1979-1-2T17:00:00.0 41.6276969038137 32.353
35 1979-1-2T18:00:00.0 79.3818377264749 30.15
36 1979-1-2T19:00:00.0 79.1672568582629 37.07
37 1979-1-2T20:00:00.0 1.48337999844262 28.525
38 1979-1-2T21:00:00.0 87.9110385474789 38.323
39 1979-1-2T22:00:00.0 38.6646421460678 23.251
40 1979-1-2T23:00:00.0 88.4920153764757 31.236
I would like to keep all rows that have the minimum "distance" per day, so for the one day shown above, one would have only one row left (the one with index value 39). I know how to collapse the data frame so that I only have the Distance column left. I can do that - if I first set the DATE as index - with
df_short = df.groupby(df.index.floor('D'))["Distance"].min()
But I also want the Value column in my final result. How do I keep all columns?
It doesn't seem to work if I do
df_short = df.groupby(df.index.floor('D')).min(["Distance"])
This does keep all the columns in the final result, but it seems like the outcome is wrong, so I'm not sure what this does.
Maybe this is already posted somewhere, but I have trouble finding it.
You can use aggregate
df_short = df.groupby(df.index.floor('D')).agg({'Distance': min, 'Value': max})
If you want the kept Value column is the same with minimum of Distance column:
df_short = df.loc[df.groupby(df.index.floor('D'))['Distance'].idxmin(), :]
Make a datetime Index:
df.DATE = pd.to_datetime(df.DATE) # If not already datetime.
df.set_index('DATE', inplace=True)
Resample and find the min Distance's location:
df.loc[df.resample('D')['Distance'].idxmin()]
Output:
Value Distance
DATE
1979-01-02 22:00:00 38.664642 23.251
I wonder if you can help me to find a solution for the following problem. Given a data frame df1 like this
d1={'L':['aaa','bbb','ccc','aaa','bbb','ddd'],
'w':[1,5,9,13,17,21],
'x':[2,6,10,14,18,22],
'y':[3,7,11,15,19,23],
'z':[4,8,12,16,20,24]}
df1=pd.DataFrame(d1)
and two dictionaries to define grouping over columns and rows
dctRowGroups={'aaa':'A','bbb':'B','ccc':'A','ddd':'B'}
dctColGroups={'w':'ALPHA','x':'BETA','y':'ALPHA','z':'BETA'}
I wanted to aggregate over columns as a first step. Applying
g2=df1.groupby(dctColGroups,axis=1)
g2.sum()
results in
but I wanted to keep the 'L' column for the next step row-wise aggregation, i.e. the result should be a dataframe df2 more like this:
What do I need to code to make this happen?
As a next step, I want to aggregate df2 over the rows using the dctRowGroups dictionary
g3=df2.groupby(dctRowGroups,axis=0)
g3.sum()
to get a final result like this:
In what way can I do all these steps in as few lines of code as possible?
Appreciate your advice on this.
Thanks a lot
Willfried.
You can do:
Firstly create df2 and insert 'L' column by using insert() method:
df2=df1.groupby(dctColGroups,axis=1).sum()
df2.insert(0,'L',df1['L']) #use this only when the order matters
#OR(use anyone of the method either insert or assign)
df2=df2.assign(L=df1['L']) #otherwise use this
Finally use assign() ,map() and groupby() method:
result=df2.assign(L=df2['L'].map(dctRowGroups)).groupby('L').sum()
Outputs:
df2:
L ALPHA BETA
0 aaa 4 6
1 bbb 12 14
2 ccc 20 22
3 aaa 28 30
4 bbb 36 38
5 ddd 44 46
result:
ALPHA BETA
L
A 52 58
B 92 98
I'm trying to group by 2 columns of which the first value has 5 different values and the second 2.
My data looks like this:
and using
df_counted = df_analysis
.groupby(['TYPE', 'RESULT'])
.size()
.sort_values(ascending=False)
.reset_index(name='COUNT')
I was able to transform it into the cases I want:
However I don't want a column for result, just for counts.
It's suppoed to be like
COUNT_TRUE COUNT_FALSE
FORWARD 21 182
BACKWARD 34 170
RIGHT 24 298
LEFT 20 242
NEUTRAL 16 82
The best I could do there was this. How do I get there?
Pandas has a feature of making a pivot table with dataframe. Your task can also be done by making pivot table.
df_counted.pivot_table(index="TYPE", columns="RESULT", values="COUNT")
Result:
Solved it and went a kind of full SQL there. It's not elegant, but it works:
df_counted is the last df from the question with the NaN values.
# drop duplicates for the first counts
df_pos = df_counted.drop_duplicates(subset=['TYPE'], keep='first').drop(columns=['COUNT_POS'])
# drop duplicates for the first counts
df_neg = df_counted.drop_duplicates(subset=['TYPE'], keep='last').drop(columns=['COUNT_NEG'])
# join on TYPE
df = df_pos.set_index('TYPE').join(df_neg.set_index('TYPE'))
If someone has a more elegant way of doing this, I'd be super interested to see it.
I'm trying to automate billing for my boss. I have to choose the highest quantity for an invoice date and client, then print that quantity in a separate column and a 0 (or blank) for the second row associated with that client. I'm trying to recreate this example:
Billing Snippet
I'm having trouble using Pandas to do this. I used a pivot table to get the max quantity for each client, then merged that data with the original to get a "max" column. That looks like this:
Dataframe snippet
My plan is to use indexes to essentially say "if the Qty is not equal to Max, then change the value to 0"
Here's my code, but I get the error "A value is trying to be set on a copy of a slice from a DataFrame" :
ad2[ad2['Qty'] != ad2['max']]['Qtrly Billing Count']=0
Any advice on how to tackle this?
Update: Tried turning off the setting that gives me the index error, but the column I want to update isn't changing. Help!
Recreating you df:
ad2 = pd.DataFrame({'Qty':[33, 47],'max':[47,47], 'Qtrly':[47,47] })
Qtrly Qty max
0 47 33 47
1 47 47 47
using loc:
ad2.loc[ad2['Qty'] != ad2['max'], 'Qtrly']=0
result:
Qtrly Qty max
0 0 33 47
1 47 47 47
Below is some data:
Test Day1 Day2 Score
A 1 2 100
B 1 3 62
C 3 4 90
D 2 4 20
E 4 5 80
I am trying to take the values from column 'day' and 'day2' and use them to select the row number for the column score. For example for Test A I would like to find the sum of 100 and 62 because that is the values of the first and second rows of score. Test B I would like to find the sum of 100, 62 and 90.
Is their anyway to do this in the Compute Variable window? Found in the menu Transform-Compute Variable?
I tried the following:
Score(MEAN(VALUE(Day1), VALUE(DAY2)))
This is not the proper way to call the cell location of Score and I received an error.
Can anyone help?
Thank you!
You really have two different datasets here. One is a dataset of scores numbered 1 through 5.
The other is a dataset that includes indexes into the score dataset. So the steps would be something like this.
First take the scores dataset and transpose it so that it has one row and 5 columns (Data>Transpose)
Then match that dataset to each case in the main dataset (Data>Merge Files>Add Variables).
Next you have to resort to using syntax directly.
You would declare a vector for the scores (VECTOR)
Finally, you use COMPUTE to index into the scores.
For your real problem, I suppose that you might have batches of scores and maybe there are some gaps. The Restructure Data Wizard can help you generalize this - convert cases into variables, but let's not go there yet.
HTH,
Jon Peck