Given a list of market data (date, open, high, low, close), how would I create a list of best and worst returns for a given period of exact time?
Where Return = df['close'] / df['close'].shift(exactly 6 years in the past), not just a set number of rows back)
The result - Top 6 Years of best rolling returns:
1/1/1975 - 1/1/1981, 345.2%
2/1/1990 - 1/31/1997, 331.5%
etc.
Then for the worst returns, the same thing
Date-Date, %
Date-Date, %
etc...
I can brute-force do it with just Python and not much Pandas, but I bet there is some cool Pandas way to more elegantly do it. Thanks in advance
try using dataframe .rolling(window =7, center).mean(). this create a window of data over 7 rows and then averages them.
Related
I have a datafile with quality scores from different suppliers over a time range of 3 years. The end goal is to use machine learning to predict the quality label (good or bad) of a shipment based on supplier information.
I want to use the mean historic quality data over a specific period of time as an input feature in this model by using pandas rolling window. the problem with this method is that pandas only allows you to create a window from t=0-x until t=0 for you rolling window as presented below:
df['average_score t-2w'] = df['score'].rolling(window='14d',closed='left').mean()
And this is were the problem comes in. For my feature I want to use quality data from a period of 2 weeks, but these 2 weeks are not the 2 weeks before the corresponding shipment, but of 2 weeks, starting from t=-4weeks , and ending on t=-2weeks.
You would imagine that this could be solved by using the same string of code but changing the window as presented below:
df['average_score t-2w'] = df['score'].rolling(window='28d' - '14d',closed='left').mean()
This, or any other type of denotation of this specific window does not seem to work.
It seems like pandas does not offer a solution to this problem, so we made a work around it with the following solution:
def time_shift_week(df):
def _avg_score_interval_func(series):
current_time = series.index[-1]
result = series[(series.index > ( current_time- pd.Timedelta(value=4, unit='w')))
& (series.index < (current_time - pd.Timedelta(value=2, unit='w')))]
return result.mean() if len(result)>0 else 0.0
temp_df = df.groupby(by=["supplier", "timestamp"], as_index=False).aggregate({"score": np.mean}).set_index('timestamp')
temp_df["w-42"] = (
temp_df
.groupby(["supplier"])
.ag_score
.apply(lambda x:
x
.rolling(window='30D', closed='both')
.apply(_avg_score_interval_func)
))
return temp_df.reset_index()
This results in a new df in which we find the average score score per supplier per timestamp, which we can subsequently merge with the original data frame to obtain the new feature.
Doing it this way seems really cumbersome and overly complicated for the task I am trying to perform. Eventhough we have found a workaround, I am wondering if there is an easier method of doing this.
Is anyone aware of a less complicated way of performing this rolling window feature extraction?
While pandas does not have the custom date offset you need, calculating the mean is pretty simple: it's just sum divided by count. You can subtract the 14-day rolling window from the 28-day rolling window:
# Some sample data. All scores are sequential for easy verification
idx = pd.MultiIndex.from_product(
[list("ABC"), pd.date_range("2020-01-01", "2022-12-31")],
names=["supplier", "timestamp"],
)
df = pd.DataFrame({"score": np.arange(len(idx))}, index=idx).reset_index()
# Now we gonna do rolling avg on score with the custom window.
# closed=left mean the current row will be excluded from the window.
score = df.set_index("timestamp").groupby("supplier")["score"]
r28 = score.rolling("28d", closed="left")
r14 = score.rolling("14d", closed="left")
avg_score = (r28.sum() - r14.sum()) / (r28.count() - r14.count())
total beginner here. If my question is irrelevant, apologies in advance, I'll remove it. So, I have a question : using pandas, I want to calculate an evolution ratio for a week data compared with the previous rolling 4 weeks mean data.
df['rolling_mean_fourweeks'] = df.rolling(4).mean().round(decimals=1)
from here I wanna create a new column for the evolution ratio based on the week data compared with the row of the rolling mean at the previous week.
what is the best way to go here? (I don't have big data) I have tried unsuccessfully with .shift() but am very foreign to .shift()... I should get NAN for week 3 (fourth week) and ~47% for fifth week.
Any suggestion for retrieving the value at row with step -1?
Thanks and have a good day!
Your idea about using shift can perfectly work. The shift(x) function simply shifts a series (a full column in your case) of x steps.
A simple way to check if the rolling_mean_fourweeks is a good predictor can be to shift Column1 and then check how it differs from rolling_mean_fourweeks:
df['column1_shifted'] = df['Column1'].shift(-1)
df['rolling_accuracy'] = ((df['column1_shifted']-df['rolling_mean_fourweeks'])
/df['rolling_mean_fourweeks'])
resulting in:
Lets say i have Dataframe, which has 200 values, prices for products. I want to run some operation on this dataframe, like calculate average price for last 10 prices.
The way i understand it, right now pandas will go through every single row and calculate average for each row. Ie first 9 rows will be Nan, then from 10-200, it would calculate average for each row.
My issue is that i need to do a lot of these calculations and performance is an issue. For that reason, i would want to run the average only on say on last 10 values (dont need more) from all values, while i want to keep those values in the dataframe. Ie i dont want to get rid of those values or create new Dataframe.
I just essentially want to do calculation on less data, so it is faster.
Is something like that possible? Hopefully the question is clear.
Building off Chicodelarose's answer, you can achieve this in a more "pandas-like" syntax.
Defining your df as follows, we get 200 prices up to within [0, 1000).
df = pd.DataFrame((np.random.rand(200) * 1000.).round(decimals=2), columns=["price"])
The bit you're looking for, though, would the following:
def add10(n: float) -> float:
"""An exceptionally simple function to demonstrate you can set
values, too.
"""
return n + 10
df["price"].iloc[-12:] = df["price"].iloc[-12:].apply(add10)
Of course, you can also use these selections to return something else without setting values, too.
>>> df["price"].iloc[-12:].mean().round(decimals=2)
309.63 # this will, of course, be different as we're using random numbers
The primary justification for this approach lies in the use of pandas tooling. Say you want to operate over a subset of your data with multiple columns, you simply need to adjust your .apply(...) to contain an axis parameter, as follows: .apply(fn, axis=1).
This becomes much more readable the longer you spend in pandas. 🙂
Given a dataframe like the following:
Price
0 197.45
1 59.30
2 131.63
3 127.22
4 35.22
.. ...
195 73.05
196 47.73
197 107.58
198 162.31
199 195.02
[200 rows x 1 columns]
Call the following to obtain the mean over the last n rows of the dataframe:
def mean_over_n_last_rows(df, n, colname):
return df.iloc[-n:][colname].mean().round(decimals=2)
print(mean_over_n_last_rows(df, 2, "Price"))
Output:
178.67
Date
GoogleAnalytics_PVS
AdobeAnalytics_PVS
6-3-2020
4802
4922
6-4-2020
5939
5932
6-5-2020
5122
5298
I have a table structured like the one above where it returns the number of page views from two sources. Ideally, I would like another column that would return a discrepancy percentage.
Am I overthinking it or could I just do something like
df['Discrep_%'] = (df['GoogleAnalytics_PVS'] - df['AdobeAnalytics_PVS'] / df['GoogleAnalytics_PVS']) x 100
Is there a better method, please let me know, thanks!
complexity wise it's the same, but here is another way. hence there can be multiple ways but the one you are applying is also the better.
df_new = [df[df.columns.difference(['GoogleAnalytics_PVS', 'AdobeAnalytics_PVS'])]/df['GoogleAnalytics_PVS'] ]*100
df_new
Right now, I see there are quick ways to get things like Sum/Avg/Max/Etc. for two or more rows or columns when building a table in GoodData.
quick total options
I am building a little table that shows last week and the week prior, and I'm trying to show the delta between them.
So if the first column is 100 and the second is 50, I want '-50'
If the first column is 25 and the second is 100, i want '75'
Is there an easy way to do this?
Let’s consider, that the first column contains result of calculating of metric #1 and the second column contains result of calculating of metric #2, you can simply create a metric #3, which would be defined as the (metric #1 - metric #2) or vice versa.