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Find minima and maxima of DataFrame by chronological order

I have a pandas data frame where I extract minima and extrema values. It work good so far, but the problem is how can I place them by Date (chronological order) into a list? They are separated into two list and I only want one price values list with them being in chronological order
import pandas as pd
import numpy as np
import yfinance
from scipy.signal import argrelextrema
import matplotlib.dates as mpl_dates
def extract_data():
ticker = 'GBPJPY=X'
ticker = yfinance.Ticker(ticker)
start_date = '2022-09-25'
end_date = '2022-10-08'
df = ticker.history(interval='1h', start=start_date, end=end_date)
df['Date'] = pd.to_datetime(df.index)
df['Date'] = df['Date'].apply(mpl_dates.date2num)
df = df.loc[:, ['Date', 'Open', 'High', 'Low', 'Close']]
# Call function to find Min-Max Extrema
find_extrema(df)
def find_extrema(df):
n = 10 # number of points to be checked before and after
# Find local peaks
df['min'] = df.iloc[argrelextrema(df.Close.values, np.less_equal,
order=n)[0]]['Close']
df['max'] = df.iloc[argrelextrema(df.Close.values, np.greater_equal,
order=n)[0]]['Close']
min_values_list = []
max_values_list = []
# Add min value to list
for item in df['min']:
check_NaN = np.isnan(item) # check if values is empty
if check_NaN == True:
pass
else:
min_values_list.append(item)
# Add max values to list
for item in df['max']:
check_NaN = np.isnan(item) # check if values is empty
if check_NaN == True:
pass
else:
max_values_list.append(item)
print(f"Min: {min_values_list}")
print(f"Max: {max_values_list}")
extract_data()

Option 1
First, use df.to_numpy to convert columns min and max to a np.array.
Get rid of all the NaN values by selecting from the array using np.logical_or applied to a boolean mask (created with np.isnan).
arr = df[['min','max']].to_numpy()
value_list = arr[np.logical_not(np.isnan(arr))].tolist()
print(value_list)
[159.7030029296875,
154.8979949951172,
160.7830047607422,
165.43800354003906,
149.55799865722656,
162.80499267578125,
156.6529998779297,
164.31900024414062,
156.125,
153.13499450683594,
161.3520050048828,
156.9340057373047,
162.52200317382812,
155.7740020751953,
160.98500061035156,
161.83700561523438]
Option 2
Rather more cumbersome:
n = 10
# get the indices for `min` and `max` in two arrays
_min = argrelextrema(df.Close.values, np.less_equal, order=n)[0]
_max = argrelextrema(df.Close.values, np.greater_equal, order=n)[0]
# create columns (assuming you need this for other purposes as well)
df['min'] = df.iloc[_min]['Close']
df['max'] = df.iloc[_max]['Close']
# create lists for `min` and `max`
min_values_list = df['min'].dropna().tolist()
max_values_list = df['max'].dropna().tolist()
# join the lists
value_list2 = min_values_list + max_values_list
value_idxs = _min.tolist() + _max.tolist()
# finally, sort `value_list2` based on `value_idxs`
value_list2 = [x for _, x in sorted(zip(value_idxs, value_list2))]
# check if result is the same:
value_list2 == value_list
# True

Assuming that you have max and min columns, what about something like this?
df['max_or_min'] = np.where(df['max'].notna(), df['max'], df['min'])
min_max_values = df['max_or_min'].dropna().values.tolist()

How to add some calculation in columns of the dataframe in python

I am having the excel sheet using the pandas.read_excel, I got the output in dataframe but I want to add the calculations in the after reading through pandas I need to ado following calculation in each x and y columns.
ratiox = (73.77481944859028 - 73.7709567323327) / 720
ratioy = (18.567453940477293 - 18.56167674097576) / 1184
mapLongitudeStart = 73.7709567323327
mapLatitudeStart = 18.567453940477293
longitude = 0, latitude = 0
longitude = (mapLongitudeStart + x1 * ratiox)) #I have take for the single column x1 value
latitude = (mapLatitudeStart - (-y1 *ratioy )) # taken column y1 value
how to apply this calculation to every column and row of x and y a which has the values it should not take the null values. And I want the new dataframe created by doing the calculation in columns

Try the below code:
import pandas as pd
import itertools
df = pd.read_excel('file_path')
dfx=df.ix[:,'x1'::2]
dfy=df.ix[:,'y1'::2]
li=[dfx.apply(lambda x:mapLongitudeStart + x * ratiox),dfy.apply(lambda y:mapLatitudeStart - (-y))]
df_new=pd.concat(li,axis=1)
df_new = df_new[list(itertools.chain(*zip(dfx.columns,dfy.columns)))]
print(df_new)
Hope this helps!

I would first recommend to reshape your data into a long format, that way you can get rid of the empty cells naturally. Also most pandas functions work better that way, because then you can use things like group by operations on all x or y or wahtever dimenstion
from itertools import chain
import pandas as pd
## this part is only to have a running example
## here you would load your excel file
D = pd.DataFrame(
np.random.randn(10,6),
columns =chain(*[ [f"x{i}", f"y{i}"] for i in range(1,4)])
)
D["rowid"] = pd.np.arange(len(D))
D = D.melt(id_vars="rowid").dropna()
D["varIndex"] = D.variable.str[1]
D["variable"] = D.variable.str[0]
D = D.set_index(["varIndex","rowid","variable"])\
.unstack("variable")\
.droplevel(0, axis=1)
So these transformations will give you a table where you have an index both for the original row id (maybe it is a time series or something else), and the variable index so x1 or x2 etc.
Now you can do your calculations either by overwintering the previous columns
## Everything here is a constant
ratiox = (73.77481944859028 - 73.7709567323327) / 720
ratioy = (18.567453940477293 - 18.56167674097576) / 1184
mapLongitudeStart = 73.7709567323327
mapLatitudeStart = 18.567453940477293
# apply the calculations directly to the columns
D.x = (mapLongitudeStart + D.x * ratiox))
D.y = (mapLatitudeStart - (-D.y * ratioy ))

Using a PeriodIndex to slice a pandas series

I have a few pandas series with PeriodIndex of varying frequency. I'd like to filter these based on another PeriodIndex of which the frequency is in principle unknown (specified directly in the example below as selectionA or selectionB, but in practice stripped from another series).
I've found 3 approaches, each with its own downside, shown in the example below. Is there a better way?
import numpy as np
import pandas as pd
y = pd.Series(np.random.random(4), index=pd.period_range('2018', '2021', freq='A'), name='speed')
q = pd.Series(np.random.random(16), index=pd.period_range('2018Q1', '2021Q4', freq='Q'), name='speed')
m = pd.Series(np.random.random(48), index=pd.period_range('2018-01', '2021-12', freq='M'), name='speed')
selectionA = pd.period_range('2018Q3', '2020Q2', freq='Q') #subset of y, q, and m
selectionB = pd.period_range('2014Q3', '2015Q2', freq='Q') #not subset of y, q, and m
#Comparing some options:
#1: filter method
#2: slicing
#3: selection based on boolean comparison
#1: problem when frequencies unequal: always returns empty series
yA_1 = y.filter(selectionA, axis=0) #Fail: empty series
qA_1 = q.filter(selectionA, axis=0)
mA_1 = m.filter(selectionA, axis=0) #Fail: empty series
yB_1 = y.filter(selectionB, axis=0)
qB_1 = q.filter(selectionB, axis=0)
mB_1 = m.filter(selectionB, axis=0)
#2: problem when frequencies unequal: wrong selection and error instead of empty result
yA_2 = y[selectionA[0]:selectionA[-1]]
qA_2 = q[selectionA[0]:selectionA[-1]]
mA_2 = m[selectionA[0]:selectionA[-1]] #Fail: selects 22 months instead of 24
yB_2 = y[selectionB[0]:selectionB[-1]] #Fail: error
qB_2 = q[selectionB[0]:selectionB[-1]]
mB_2 = m[selectionB[0]:selectionB[-1]] #Fail: error
#3: works, but very verbose
yA_3 =y[(y.index >= selectionA[0].start_time) & (y.index <= selectionA[-1].end_time)]
qA_3 =q[(q.index >= selectionA[0].start_time) & (q.index <= selectionA[-1].end_time)]
mA_3 =m[(m.index >= selectionA[0].start_time) & (m.index <= selectionA[-1].end_time)]
yB_3 =y[(y.index >= selectionB[0].start_time) & (y.index <= selectionB[-1].end_time)]
qB_3 =q[(q.index >= selectionB[0].start_time) & (q.index <= selectionB[-1].end_time)]
mB_3 =m[(m.index >= selectionB[0].start_time) & (m.index <= selectionB[-1].end_time)]
Many thanks

I've solved it by adding start_time and end_time to the slice range:
yA_2fixed = y[selectionA[0].start_time: selectionA[-1].end_time]
qA_2fixed = q[selectionA[0].start_time: selectionA[-1].end_time]
mA_2fixed = m[selectionA[0].start_time: selectionA[-1].end_time] #now has 24 rows
yB_2fixed = y[selectionB[0].start_time: selectionB[-1].end_time] #doesn't fail; returns empty series
qB_2fixed = q[selectionB[0].start_time: selectionB[-1].end_time]
mB_2fixed = m[selectionB[0].start_time: selectionB[-1].end_time] #doesn't fail; returns empty series
But if there's a more concise way to write this, I'm still all ears. I especially would like to know if it's possible to do this filtering in a way that is more 'native' to the PeriodIndex, i.e., not converting it into datetime instances first with the start_time and end_time attributes.

How to fill missing date in timeSeries

Here's what my data looks like:
There are daily records, except for a gap from 2017-06-12 to 2017-06-16.
df2['timestamp'] = pd.to_datetime(df['timestamp'])
df2['timestamp'] = df2['timestamp'].map(lambda x:
datetime.datetime.strftime(x,'%Y-%m-%d'))
df2 = df2.convert_objects(convert_numeric = True)
df2 = df2.groupby('timestamp', as_index = False).sum()
I need to fill this missing gap and others with values for all fields (e.g. timestamp, temperature, humidity, light, pressure, speed, battery_voltage, etc...).
How can I accomplish this with Pandas?
This is what I have done before
weektime = pd.date_range(start = '06/04/2017', end = '12/05/2017', freq = 'W-SUN')
df['week'] = 'nan'
df['weektemp'] = 'nan'
df['weekhumidity'] = 'nan'
df['weeklight'] = 'nan'
df['weekpressure'] = 'nan'
df['weekspeed'] = 'nan'
df['weekbattery_voltage'] = 'nan'
for i in range(0,len(weektime)):
df['week'][i+1] = weektime[i]
df['weektemp'][i+1] = df['temperature'].iloc[7*i+1:7*i+7].sum()
df['weekhumidity'][i+1] = df['humidity'].iloc[7*i+1:7*i+7].sum()
df['weeklight'][i+1] = df['light'].iloc[7*i+1:7*i+7].sum()
df['weekpressure'][i+1] = df['pressure'].iloc[7*i+1:7*i+7].sum()
df['weekspeed'][i+1] = df['speed'].iloc[7*i+1:7*i+7].sum()
df['weekbattery_voltage'][i+1] =
df['battery_voltage'].iloc[7*i+1:7*i+7].sum()
i = i + 1
The value of sum is not correct. Cause the value of 2017-06-17 is a sum of 2017-06-12 to 2017-06-16. I do not want to add them again. This gap is not only one gap in the period. I want to fill all of them.

Here is a function I wrote that might be helpful to you. It looks for inconsistent jumps in time and fills them in. After using this function, try using a linear interpolation function (pandas has a good one) to fill in your null data values. Note: Numpy arrays are much faster to iterate over and manipulate than Pandas dataframes, which is why I switch between the two.
import numpy as np
import pandas as pd
data_arr = np.array(your_df)
periodicity = 'daily'
def fill_gaps(data_arr, periodicity):
rows = data_arr.shape[0]
data_no_gaps = np.copy(data_arr) #avoid altering the thing you're iterating over
data_no_gaps_idx = 0
for row_idx in np.arange(1, rows): #iterate once for each row (except the first record; nothing to compare)
oldtimestamp_str = str(data_arr[row_idx-1, 0])
oldtimestamp = np.datetime64(oldtimestamp_str)
currenttimestamp_str = str(data_arr[row_idx, 0])
currenttimestamp = np.datetime64(currenttimestamp_str)
period = currenttimestamp - oldtimestamp
if period != np.timedelta64(900,'s') and period != np.timedelta64(3600,'s') and period != np.timedelta64(86400,'s'):
if periodicity == 'quarterly':
desired_period = 900
elif periodicity == 'hourly':
desired_period = 3600
elif periodicity == 'daily':
desired_period = 86400
periods_missing = int(period / np.timedelta64(desired_period,'s'))
for missing in np.arange(1, periods_missing):
new_time_orig = str(oldtimestamp + missing*(np.timedelta64(desired_period,'s')))
new_time = new_time_orig.replace('T', ' ')
data_no_gaps = np.insert(data_no_gaps, (data_no_gaps_idx + missing),
np.array((new_time, np.nan, np.nan, np.nan, np.nan, np.nan)), 0) # INSERT VALUES YOU WANT IN THE NEW ROW
data_no_gaps_idx += (periods_missing-1) #incriment the index (zero-based => -1) in accordance with added rows
data_no_gaps_idx += 1 #allow index to change as we iterate over original data array (main for loop)
#create a dataframe:
data_arr_no_gaps = pd.DataFrame(data=data_no_gaps, index=None,columns=['Time', 'temp', 'humidity', 'light', 'pressure', 'speed'])
return data_arr_no_gaps

Fill time gaps and nulls
Use the function below to ensure expected date sequence exists, and then use forward fill to fill in nulls.
import pandas as pd
import os
def fill_gaps_and_nulls(df, freq='1D'):
'''
General steps:
A) check for extra dates (out of expected frequency/sequence)
B) check for missing dates (based on expected frequency/sequence)
C) use forwardfill to fill nulls
D) use backwardfill to fill remaining nulls
E) append to file
'''
#rename the timestamp to 'date'
df.rename(columns={"timestamp": "date"})
#sort to make indexing faster
df = df.sort_values(by=['date'], inplace=False)
#create an artificial index of dates at frequency = freq, with the same beginning and ending as the original data
all_dates = pd.date_range(start=df.date.min(), end=df.date.max(), freq=freq)
#record column names
df_cols = df.columns
#delete ffill_df.csv so we can begin anew
try:
os.remove('ffill_df.csv')
except FileNotFoundError:
pass
#check for extra dates and/or dates out of order. print warning statement for log
extra_dates = set(df.date).difference(all_dates)
#if there are extra dates (outside of expected sequence/frequency), deal with them
if len(extra_dates) > 0:
#############################
#INSERT DESIRED BEHAVIOR HERE
print('WARNING: Extra date(s):\n\t{}\n\t Shifting highlighted date(s) back by 1 day'.format(extra_dates))
for date in extra_dates:
#shift extra dates back one day
df.date[df.date == date] = date - pd.Timedelta(days=1)
#############################
#check the artificial date index against df to identify missing gaps in time and fill them with nulls
gaps = all_dates.difference(set(df.date))
print('\n-------\nWARNING: Missing dates: {}\n-------\n'.format(gaps))
#if there are time gaps, deal with them
if len(gaps) > 0:
#initialize df of correct size, filled with nulls
gaps_df = pd.DataFrame(index=gaps, columns=df_cols.drop('date')) #len(index) sets number of rows
#give index a name
gaps_df.index.name = 'date'
#add the region and type
gaps_df.region = r
gaps_df.type = t
#remove that index so gaps_df and df are compatible
gaps_df.reset_index(inplace=True)
#append gaps_df to df
new_df = pd.concat([df, gaps_df])
#sort on date
new_df.sort_values(by='date', inplace=True)
#fill nulls
new_df.fillna(method='ffill', inplace=True)
new_df.fillna(method='bfill', inplace=True)
#append to file
new_df.to_csv('ffill_df.csv', mode='a', header=False, index=False)
return df_cols, regions, types, all_dates

Spatial temporal query in python with many records

I have a dataframe of 600 000 x/y points with date-time information, along another field 'status', with extra descriptive information
My objective is, for each record:
sum column 'status' by records that are within a certain spatial temporal buffer
the specific buffer is within t - 8 hours and < 100 meters
Currently I have the data in a pandas data frame.
I could, loop through the rows, and for each record, subset the dates of interest, then calculate a distances and restrict the selection further. However that would still be quite slow with so many records.
THIS TAKES 4.4 hours to run.
I can see that I could create a 3 dimensional kdtree with x, y, date as epoch time. However, I am not certain how to restrict the distances properly when incorporating dates and geographic distances.
Here is some reproducible code for you guys to test on:
Import
import numpy.random as npr
import numpy
import pandas as pd
from pandas import DataFrame, date_range
from datetime import datetime, timedelta
Create data
np.random.seed(111)
Function to generate test data
def CreateDataSet(Number=1):
Output = []
for i in range(Number):
# Create a date range with hour frequency
date = date_range(start='10/1/2012', end='10/31/2012', freq='H')
# Create long lat data
laty = npr.normal(4815862, 5000,size=len(date))
longx = npr.normal(687993, 5000,size=len(date))
# status of interest
status = [0,1]
# Make a random list of statuses
random_status = [status[npr.randint(low=0,high=len(status))] for i in range(len(date))]
# user pool
user = ['sally','derik','james','bob','ryan','chris']
# Make a random list of users
random_user = [user[npr.randint(low=0,high=len(user))] for i in range(len(date))]
Output.extend(zip(random_user, random_status, date, longx, laty))
return pd.DataFrame(Output, columns = ['user', 'status', 'date', 'long', 'lat'])
#Create data
data = CreateDataSet(3)
len(data)
#some time deltas
before = timedelta(hours = 8)
after = timedelta(minutes = 1)
Function to speed up
def work(df):
output = []
#loop through data index's
for i in range(0, len(df)):
l = []
#first we will filter out the data by date to have a smaller list to compute distances for
#create a mask to query all dates between range for date i
date_mask = (df['date'] >= df['date'].iloc[i]-before) & (df['date'] <= df['date'].iloc[i]+after)
#create a mask to query all users who are not user i (themselves)
user_mask = df['user']!=df['user'].iloc[i]
#apply masks
dists_to_check = df[date_mask & user_mask]
#for point i, create coordinate to calculate distances from
a = np.array((df['long'].iloc[i], df['lat'].iloc[i]))
#create array of distances to check on the masked data
b = np.array((dists_to_check['long'].values, dists_to_check['lat'].values))
#for j in the date queried data
for j in range(1, len(dists_to_check)):
#compute the ueclidean distance between point a and each point of b (the date masked data)
x = np.linalg.norm(a-np.array((b[0][j], b[1][j])))
#if the distance is within our range of interest append the index to a list
if x <=100:
l.append(j)
else:
pass
try:
#use the list of desired index's 'l' to query a final subset of the data
data = dists_to_check.iloc[l]
#summarize the column of interest then append to output list
output.append(data['status'].sum())
except IndexError, e:
output.append(0)
#print "There were no data to add"
return pd.DataFrame(output)
Run code and time it
start = datetime.now()
out = work(data)
print datetime.now() - start
Is there a way to do this query in a vectorized way? Or should I be chasing another technique.
<3

Here is what at least somewhat solves my problem. Since the loop can operate on different parts of the data independently, parallelization makes sense here.
using Ipython...
from IPython.parallel import Client
cli = Client()
cli.ids
cli = Client()
dview=cli[:]
with dview.sync_imports():
import numpy as np
import os
from datetime import timedelta
import pandas as pd
#We also need to add the time deltas and output list into the function as
#local variables as well as add the Ipython.parallel decorator
#dview.parallel(block=True)
def work(df):
before = timedelta(hours = 8)
after = timedelta(minutes = 1)
output = []
final time 1:17:54.910206, about 1/4 original time
I would still be very interested for anyone to suggest small speed improvements within the body of the function.