Develop Reference

pmdarima: Apply .predict method via .groupby and .apply to auto_arima output stored rowwise in a pd.DataFrame

I'm using auto_arima via pmdarima to fit multiple time series via a groupby. This is to say, I have a pd.DataFrame of stacked time-indexed data, grouped by variable variable, and have successfully applied transform(pm.auto_arima) to each. The reproducible example finds boring best ARIMA models, but the idea seems to work. I now want to apply .predict() similarly, but cannot get it to play nice with apply / lambda(x) / their combinations.
The code below works until the # Forecasting - help! section. I'm having trouble catching the correct object (apparently) in the apply. How might I adapt one of test1, test2, or test3 to get what I want? Or, is there some other best-practice construct to consider? Is it better across columns (without a melt)? Or via a loop?
Ultimately, I hope that test1, say, is a stacked pd.DataFrame (or pd.Series at least) with 8 rows: 4 forecasted values for each of the 2 time series in this example, with an identifier column variable (possibly tacked on after the fact).
import pandas as pd
import pmdarima as pm
import itertools
# Get data - this is OK.
url = 'https://raw.githubusercontent.com/nickdcox/learn-airline-delays/main/delays_2018.csv'
keep = ['arr_flights', 'arr_cancelled']
# Setup data - this is OK.
df = pd.read_csv(url, index_col=0)
df.index = pd.to_datetime(df.index, format = "%Y-%m")
df = df[keep]
df = df.sort_index()
df = df.loc['2018']
df = df.groupby(df.index).sum()
df.reset_index(inplace = True)
df = df.melt(id_vars = 'date', value_vars = df.columns.to_list()[1:])
# Fit auto.arima for each time series - this is OK.
fit = df.groupby('variable')['value'].transform(pm.auto_arima).drop_duplicates()
fit = fit.to_frame(name = 'model')
fit['variable'] = keep
fit.reset_index(drop = True, inplace = True)
# Setup forecasts - this is OK.
max_date = df.date.max()
dr = pd.to_datetime(pd.date_range(max_date, periods = 4 + 1, freq = 'MS').tolist()[1:])
yhat = pd.DataFrame(list(itertools.product(keep, dr)), columns = ['variable', 'date'])
yhat.set_index('date', inplace = True)
# Forecasting - help! - Can't get any of these to work.
def predict_fn(obj):
return(obj.loc[0].predict(4))
predict_fn(fit.loc[fit['variable'] == 'arr_flights']['model']) # Appears to work!
test1 = fit.groupby('variable')['model'].apply(lambda x: x.predict(n_periods = 4)) # Try 1: 'Series' object has no attribute 'predict'.
test2 = fit.groupby('variable')['model'].apply(lambda x: x.loc[0].predict(n_periods = 4)) # Try 2: KeyError
test3 = fit.groupby('variable')['model'].apply(predict_fn) # Try 3: KeyError

Can this pandas workflow be converted to dask?

Please be nice - I'm not a proper programmer, I'm a scientist and I've read as many docs on this as I can find (they're a bit sparse).
I'm trying to convert this pandas code into dash because my input file is ~0.5TB with gz and it loads too slowly in native pandas. I have a 3 TB machine, btw.
This is an example of what I'm doing with pandas:
df = pd.DataFrame([['chr1',33329,17,'''33)'6'4?1&AB=?+..''','''X%&=E&!%,0("&"Y&!'''],
['chr1',33330,15,'''6+'/7=1#><C1*'*''','''X%=E!%,("&"Y&&!'''],
['chr1',33331,13,'''2*3A#/9#CC3--''','''X%E!%,("&"Y&!'''],
['chr1',33332,1,'''4**(,:3)+7-#<(0-''','''X%&E&!%,0("&"Y&!'''],
['chr1',33333,2,'''66(/C=*42A:.&*''','''X%=&!%0("&"&&!''']],
columns = ['chrom','pos','depth','phred','map'])
df.loc[:,'phred'] = [(sum(map(ord,i))-len(i)*33)/len(i) for i in df.loc[:,"phred"]]
df.loc[:,"map"] = [(sum(map(ord,i)))/len(i) for i in df.loc[:,"map"]]
df = df.astype({'phred': 'int32', 'map': 'int32'})
df.query('(depth < 10) | (phred < 7) | (map < 10)', inplace=True)
for chrom, df_tmp in df.groupby('chrom'):
df_end = df_tmp[~((df_tmp.pos.shift(0) == df_tmp.pos.shift(-1)-1))]
df_start = df_tmp[~((df_tmp.pos.shift(0) == df_tmp.pos.shift(+1)+1))]
for start, end in zip(df_start.pos, df_end.pos):
print (start, end)
Gives
33332 33333
This works (to find regions of a cancer genome with no data) and it's optimised as much as I know how.
I load the real thing like:
df = pd.read_csv(
'/Users/liamm/Downloads/test_head33333.tsv.gz',
sep='\t',
header=None,
index_col=None,
usecols=[0,1,3,5,6],
names = ['chrom','pos','depth','phred','map']
)
and I can do the same with Dask (way faster!):
df = dd.read_csv(
'/Users/liamm/Downloads/test_head33333.tsv.gz',
sep='\t',
header=None,
usecols=[0,1,3,5,6],
compression='gzip',
blocksize=None,
names = ['chrom','pos','depth','phred','map']
)
but i'm stuck here:
ff=[(sum(map(ord,i))-len(i)*33)/len(i) for i in df.loc[:,"phred"]]
df['phred'] = ff
Error: Column assignment doesn't support type list
Question - is this sort of thing possible? If so are there good tutes somewhere? I need to convert the whole block of pandas code above.
Thanks in advance!

You created list comprehensions to transform 'Fred' and 'map'; I converted these list comps to functions, and wrapped the functions in np.vectorize().
def func_p(p):
return (sum(map(ord, p)) - len(p) * 33) / len(p)
def func_m(m):
return (sum(map(ord, m))) / len(m)
vec_func_p = np.vectorize(func_p)
vec_func_m = np.vectorize(func_m)
np.vectorize() does not make code faster, but it does let you write a function with scalar inputs and outputs, and convert it to a function that takes array inputs and outputs.
The benefit is that we can now pass pandas Series to these functions (I also added the type conversion to this step):
df.loc[:, 'phred'] = vec_func_p( df.loc[:, 'phred']).astype(np.int32)
df.loc[:, 'map'] = vec_func_m( df.loc[:, 'map']).astype(np.int32)
Replacing the list comprehensions with these new functions gives the same results as your version (33332 33333).

#rpanai noted that you could eliminate the for loops. The following example uses groupby() (and a couple helper columns) to find the start and end position for each contiguous sequence of positions.
Using only pandas built-in functions should be compatible with Dask (and fast).
First, create demo data frame with multiple chromosomes and multiple contiguous blocks of positions:
data1 = {
'chrom' : 'chrom_1',
'pos' : [1000, 1001, 1002,
2000, 2001, 2002, 2003]}
data2 = {
'chrom' : 'chrom_2',
'pos' : [30000, 30001, 30002, 30003, 30004,
40000, 40001, 40002, 40003, 40004, 40005]}
df = pd.DataFrame(data1).append( pd.DataFrame(data2) )
Second, create two helper functions:
rank is a sequential counter for each group;
key is constant for positions in a contiguous 'run' of positions.
df['rank'] = df.groupby('chrom')['pos'].rank(method='first')
df['key'] = df['pos'] - df['rank']
Third, group by chrom and key to create a groupby object for each contiguous block of positions, then use min and max to find start and end value for the positions.
result = (df.groupby(['chrom', 'key'])['pos']
.agg(['min', 'max'])
.droplevel('key')
.rename(columns={'min': 'start', 'max': 'end'})
)
print(result)
start end
chrom
chrom_1 1000 1002
chrom_1 2000 2003
chrom_2 30000 30004
chrom_2 40000 40005

Trying to find Mean returns for following stocks from yahoo finance

tickers = ['BIOCON.NS', 'HDFCBANK.NS', 'RELIANCE.NS', 'RADICO.NS', 'LTI.NS', 'TCS.NS', 'DRREDDY.NS','BAJFINANCE.NS']
pfolio_data = pd.DataFrame()
for t in tickers:
pfolio_data[t] = wb.DataReader(t, data_source='yahoo', start ='2017-1-1')['Adj Close']
pfolio_data_returns= (pfolio_data/pfolio_data.shift(1))-1
pfolio_data_returns
pfolio_data_returns[[[[[[[['BIOCON.NS', 'HDFCBANK.NS', 'RELIANCE.NS', 'RADICO.NS', 'LTI.NS', 'TCS.NS', 'DRREDDY.NS','BAJFINANCE.NS']]]]]]]].mean()
the last code shows me error -
unhashable type: 'list'
how should i go ahead with it ?

What does the data in pfolio_data_returns look like ?
Maybe this is what you are looking for:
Type error: unhashable type 'list' while selecting subset from specific columns pandas dataframe

This should work:
import pandas as pd
import pandas_datareader as wb
tickers = ['BIOCON.NS', 'HDFCBANK.NS', 'RELIANCE.NS', 'RADICO.NS', 'LTI.NS', 'TCS.NS', 'DRREDDY.NS','BAJFINANCE.NS']
data = {ticker: wb.DataReader(ticker, data_source='yahoo', start ='2017-1-1')['Adj Close'] for ticker in tickers}
df_raw = pd.DataFrame(data)
df_returns = (
df_raw
.apply(lambda df: df/df.shift(1)-1)
)
df_returns.mean()
Out[28]:
BIOCON.NS 0.001252
HDFCBANK.NS 0.000694
RELIANCE.NS 0.001477
RADICO.NS 0.001759
LTI.NS 0.001306
TCS.NS 0.000831
DRREDDY.NS 0.000503
BAJFINANCE.NS 0.001425
dtype: float64
I changed the way you create your dataframe. Looks a lot cleaner with a dictionary comprehension. df_raw contains raw data, df_returns computes the returns and the following .mean() statement computes the mean of each column.

There are too many opening/closing brackets. The following works:
pfolio_data_returns[['BIOCON.NS', 'HDFCBANK.NS', 'RELIANCE.NS', 'RADICO.NS', 'LTI.NS', 'TCS.NS', 'DRREDDY.NS','BAJFINANCE.NS']].mean().

Parsing JSON in Pandas

I need to extract the following json:
{"PhysicalDisks":[{"Status":"SMART Passed","Name":"/dev/sda"}]}
{"PhysicalDisks":[{"Status":"SMART Passed","Name":"/dev/sda"},{"Status":"SMART Passed","Name":"/dev/sdb"}]}
{"PhysicalDisks":[{"Status":"SMART Passed","Name":"/dev/sda"},{"Status":"SMART Passed","Name":"/dev/sdb"}]}
{"PhysicalDisks":[{"Name":"disk0","Status":"Passed"},{"Name":"disk1","Status":"Passed"}]}
{"PhysicalDisks":[{"Name":"disk0","Status":"Failed"},{"Name":"disk1","Status":"not supported"}]}
{"PhysicalDisks":[{"Name":"disk0","Status":"Passed"}]}
Name: raw_results, dtype: object
Into separate columns. I don't know how many disks per result there might be in future. What would be the best way here?
I tried the following:
d = raw_res['raw_results'].map(json.loads).apply(pd.Series).add_prefix('raw_results.')
Gives me:
Example output might be something like
Better way would be to add each disk check as an additional row into dataframe with the same checkid as the row it was extracted from. So for 3 disks in results it will generate 3 rows 1 per disk
UPDATE
This code
# This works
dfs = []
def json_to_df(row, json_col):
json_df = pd.read_json(row[json_col])
dfs.append(json_df.assign(**row.drop(json_col)))
df['raw_results'].replace("{}", pd.np.nan, inplace=True)
df = df.dropna()
df.apply(json_to_df, axis=1, json_col='raw_results')
df = pd.concat(dfs)
df.head()
Adds an extra row for each disk (sda, sdb etc.)
So now I would need to split this column into 2: Status and Name.

df1 = df["PhysicalDisks"].apply(pd.Series)
df_final = pd.concat([df, df1], axis = 1).drop('PhysicalDisks', axis = 1)
df_final.head()

Python Pandas filtering dataframe on date

I am trying to manipulate a CSV file on a certain date in a certain column.
I am using pandas (total noob) for that and was pretty successful until i got to dates.
The CSV looks something like this (with more columns and rows of course).
These are the columns:
Circuit
Status
Effective Date
These are the values:
XXXX001
Operational
31-DEC-2007
I tried dataframe query (which i use for everything else) without success.
I tried dataframe loc (which worked for everything else) without success.
How can i get all rows that are older or newer from a given date? If i have other conditions to filter the dataframe, how do i combine them with the date filter?
Here's my "raw" code:
import pandas as pd
# parse_dates = ['Effective Date']
# dtypes = {'Effective Date': 'str'}
df = pd.read_csv("example.csv", dtype=object)
# , parse_dates=parse_dates, infer_datetime_format=True
# tried lot of suggestions found on SO
cols = df.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
df.columns = cols
status1 = 'Suppressed'
status2 = 'Order Aborted'
pool = '2'
region = 'EU'
date1 = '31-DEC-2017'
filt_df = df.query('Status != #status1 and Status != #status2 and Pool == #pool and Region_A == #region')
filt_df.reset_index(drop=True, inplace=True)
filt_df.to_csv('filtered.csv')
# this is working pretty well
supp_df = df.query('Status == #status1 and Effective_Date < #date1')
supp_df.reset_index(drop=True, inplace=True)
supp_df.to_csv('supp.csv')
# this is what is not working at all
I tried many approaches, but i was not able to put it together. This is just one of many approaches i tried.. so i know it is perhaps completely wrong, as no date parsing is used.
supp.csv will be saved, but the dates present are all over the place, so there's no match with the "logic" in this code.
Thanks for any help!

Make sure you convert your date to datetime and then filter slice on it.
df['Effective Date'] = pd.to_datetime(df['Effective Date'])
df[df['Effective Date'] < '2017-12-31']
#This returns all the values with dates before 31th of December, 2017.
#You can also use Query