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Pandas - Fill in Missing Column Values Regression - python

I have a data frame 'df' that has missing column values. I want to fill in the missing/NaN values in the Avg Monthly Long Distance Charges column through prediction (regression) using the other column values. Then, replace the NaN values with the new values found.
Data frame: 'df'
Customer ID,Gender,Age,Married,Number of Dependents,City,Zip Code,Latitude,Longitude,Number of Referrals,Tenure in Months,Offer,Phone Service,Avg Monthly Long Distance Charges,Multiple Lines,Internet Service,Internet Type,Avg Monthly GB Download,Online Security,Online Backup,Device Protection Plan,Premium Tech Support,Streaming TV,Streaming Movies,Streaming Music,Unlimited Data,Contract,Paperless Billing,Payment Method,Monthly Charge,Total Charges,Total Refunds,Total Extra Data Charges,Total Long Distance Charges,Total Revenue,Customer Status,Churn Category,Churn Reason
0002-ORFBO,Female,37,Yes,0,Frazier Park,93225,34.827662,-118.999073,2,9,None,Yes,42.39,No,Yes,Cable,16,No,Yes,No,Yes,Yes,No,No,Yes,One Year,Yes,Credit Card,65.6,593.3,0,0,381.51,974.81,Stayed,,
0003-MKNFE,Male,46,No,0,Glendale,91206,34.162515,-118.203869,0,9,None,Yes,10.69,Yes,Yes,Cable,10,No,No,No,No,No,Yes,Yes,No,Month-to-Month,No,Credit Card,-4,542.4,38.33,10,96.21,610.28,Stayed,,
0004-TLHLJ,Male,50,No,0,Costa Mesa,92627,33.645672,-117.922613,0,4,Offer E,Yes,33.65,No,Yes,Fiber Optic,30,No,No,Yes,No,No,No,No,Yes,Month-to-Month,Yes,Bank Withdrawal,73.9,280.85,0,0,134.6,415.45,Churned,Competitor,Competitor had better devices
0011-IGKFF,Male,78,Yes,0,Martinez,94553,38.014457,-122.115432,1,13,Offer D,Yes,27.82,No,Yes,Fiber Optic,4,No,Yes,Yes,No,Yes,Yes,No,Yes,Month-to-Month,Yes,Bank Withdrawal,98,1237.85,0,0,361.66,1599.51,Churned,Dissatisfaction,Product dissatisfaction
0013-EXCHZ,Female,75,Yes,0,Camarillo,93010,34.227846,-119.079903,3,3,None,Yes,7.38,No,Yes,Fiber Optic,11,No,No,No,Yes,Yes,No,No,Yes,Month-to-Month,Yes,Credit Card,83.9,267.4,0,0,22.14,289.54,Churned,Dissatisfaction,Network reliability
0013-MHZWF,Female,23,No,3,Midpines,95345,37.581496,-119.972762,0,9,Offer E,Yes,16.77,No,Yes,Cable,73,No,No,No,Yes,Yes,Yes,Yes,Yes,Month-to-Month,Yes,Credit Card,69.4,571.45,0,0,150.93,722.38,Stayed,,
0013-SMEOE,Female,67,Yes,0,Lompoc,93437,34.757477,-120.550507,1,71,Offer A,Yes,9.96,No,Yes,Fiber Optic,14,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Two Year,Yes,Bank Withdrawal,109.7,7904.25,0,0,707.16,8611.41,Stayed,,
0014-BMAQU,Male,52,Yes,0,Napa,94558,38.489789,-122.27011,8,63,Offer B,Yes,12.96,Yes,Yes,Fiber Optic,7,Yes,No,No,Yes,No,No,No,No,Two Year,Yes,Credit Card,84.65,5377.8,0,20,816.48,6214.28,Stayed,,
0015-UOCOJ,Female,68,No,0,Simi Valley,93063,34.296813,-118.685703,0,7,Offer E,Yes,10.53,No,Yes,DSL,21,Yes,No,No,No,No,No,No,Yes,Two Year,Yes,Bank Withdrawal,48.2,340.35,0,0,73.71,414.06,Stayed,,
0016-QLJIS,Female,43,Yes,1,Sheridan,95681,38.984756,-121.345074,3,65,None,Yes,28.46,Yes,Yes,Cable,14,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Two Year,Yes,Credit Card,90.45,5957.9,0,0,1849.9,7807.8,Stayed,,
0017-DINOC,Male,47,No,0,Rancho Santa Fe,92091,32.99356,-117.207121,0,54,None,No,,,Yes,Cable,10,Yes,No,No,Yes,Yes,No,No,Yes,Two Year,No,Credit Card,45.2,2460.55,0,0,0,2460.55,Stayed,,
0017-IUDMW,Female,25,Yes,2,Sunnyvale,94086,37.378541,-122.020456,2,72,None,Yes,16.01,Yes,Yes,Fiber Optic,59,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Yes,Two Year,Yes,Credit Card,116.8,8456.75,0,0,1152.72,9609.47,Stayed,,
0018-NYROU,Female,58,Yes,0,Antelope,95843,38.715498,-121.363411,0,5,None,Yes,18.65,No,Yes,Fiber Optic,10,No,No,No,No,No,No,No,Yes,Month-to-Month,Yes,Bank Withdrawal,68.95,351.5,0,0,93.25,444.75,Stayed,,
0019-EFAEP,Female,32,No,0,La Mesa,91942,32.782501,-117.01611,0,72,Offer A,Yes,2.25,Yes,Yes,Fiber Optic,16,Yes,Yes,Yes,No,Yes,No,No,Yes,Two Year,Yes,Bank Withdrawal,101.3,7261.25,0,0,162,7423.25,Stayed,,
0019-GFNTW,Female,39,No,0,Los Olivos,93441,34.70434,-120.02609,0,56,None,No,,,Yes,DSL,19,Yes,Yes,Yes,Yes,No,No,No,Yes,Two Year,No,Bank Withdrawal,45.05,2560.1,0,0,0,2560.1,Stayed,,
0020-INWCK,Female,58,Yes,2,Woodlake,93286,36.464635,-119.094348,9,71,Offer A,Yes,27.26,Yes,Yes,Fiber Optic,12,No,Yes,Yes,No,No,Yes,Yes,Yes,Two Year,Yes,Credit Card,95.75,6849.4,0,0,1935.46,8784.86,Stayed,,
0020-JDNXP,Female,52,Yes,1,Point Reyes Station,94956,38.060264,-122.830646,0,34,None,No,,,Yes,DSL,20,Yes,No,Yes,Yes,Yes,Yes,Yes,Yes,One Year,No,Credit Card,61.25,1993.2,0,0,0,1993.2,Stayed,,
0021-IKXGC,Female,72,No,0,San Marcos,92078,33.119028,-117.166036,0,1,Offer E,Yes,7.77,Yes,Yes,Fiber Optic,22,No,No,No,No,No,No,No,Yes,One Year,Yes,Bank Withdrawal,72.1,72.1,0,0,7.77,79.87,Joined,,
0022-TCJCI,Male,79,No,0,Daly City,94015,37.680844,-122.48131,0,45,None,Yes,10.67,No,Yes,DSL,17,Yes,No,Yes,No,No,Yes,No,Yes,One Year,No,Credit Card,62.7,2791.5,0,0,480.15,3271.65,Churned,Dissatisfaction,Limited range of services
My code:
# Let X = predictor variable and y = target variable
X = pd.DataFrame(df[['Monthly Charge', 'Total Charges', 'Total Long Distance Charges']])
y = pd.DataFrame(df[['Avg Monthly Long Distance Charges']])
# Add a constant variable to the predictor variables
X = sm.add_constant(X)
model01 = sm.OLS(y, X).fit()
df['Avg Monthly Long Distance Charges'].fillna(sm.OLS(y, X).fit())
My code output:
0 42.39
1 10.69
2 33.65
3 27.82
4 7.38
...
7038 46.68
7039 16.2
7040 18.62
7041 2.12
7042 <statsmodels.regression.linear_model.Regressio...
Name: Avg Monthly Long Distance Charges, Length: 7043, dtype: object
My code outputs this, but does not print this into the original data frame. How do I do this? Thanks.

Related

So I have an excel file containing data on a specific stock.
My excel file contains about 2 months of data, it monitors the Open price, Close price, High Price, Low Price and Volume of trades in 5 minute intervals, so there are about 3000 rows in my file.
I want to calculate the RSI (or EMA if it's easier) of a stock daily, I'm making a summary table that collects the daily data so it converts my table of 3000+ rows into a table with only about 60 rows (each row represents one day).
Essentially I want some sort of code that sorts the excel data by date then calculates the RSI as a single value for that day. RSI is given by: 100-(100/(1+RS)) where RS = average gain of up periods/average loss of down periods.
Note: My excel uses 'Datetime' so each row's 'Datetime' looks something like '2022-03-03 9:30-5:00' and the next row would be '2022-03-03 9:35-5:00', etc. So the code needs to just look at the date and ignore the time I guess.
Some code to maybe help understand what I'm looking for:
So here I'm calling my excel file, I want the code to take the called excel file, group data by date and then calculate the RSI of each day using the formula I wrote above.
dat = pd.read_csv('AMD_5m.csv',index_col='Datetime',parse_dates=['Datetime'],
date_parser=lambda x: pd.to_datetime(x, utc=True))
dates = backtest.get_dates(dat.index)
#create a summary table
cols = ['Num. Obs.', 'Num. Trade', 'PnL', 'Win. Ratio','RSI'] #add addtional fields if necessary
summary_table = pd.DataFrame(index = dates, columns=cols)
# loop backtest by dates
This is the code I used to fill out the other columns in my summary table, I'll put my SMA (simple moving average) function below.
for d in dates:
this_dat = dat.loc[dat.index.date==d]
#find the number of observations in date d
summary_table.loc[d]['Num. Obs.'] = this_dat.shape[0]
#get trading (i.e. position holding) signals
signals = backtest.SMA(this_dat['Close'].values, window=10)
#find the number of trades in date d
summary_table.loc[d]['Num. Trade'] = np.sum(np.diff(signals)==1)
#find PnLs for 100 shares
shares = 100
PnL = -shares*np.sum(this_dat['Close'].values[1:]*np.diff(signals))
if np.sum(np.diff(signals))>0:
#close position at market close
PnL += shares*this_dat['Close'].values[-1]
summary_table.loc[d]['PnL'] = PnL
#find the win ratio
ind_in = np.where(np.diff(signals)==1)[0]+1
ind_out = np.where(np.diff(signals)==-1)[0]+1
num_win = np.sum((this_dat['Close'].values[ind_out]-this_dat['Close'].values[ind_in])>0)
if summary_table.loc[d]['Num. Trade']!=0:
summary_table.loc[d]['Win. Ratio'] = 1. *num_win/summary_table.loc[d]['Num. Trade']
This is my function for calculating Simple Moving Average. I was told to try and adapt this for RSI or for EMA (Exponential Moving Average). Apparently adapting this for EMA isn't too troublesome but I can't figure it out.
def SMA(p,window=10,signal_type='buy only'):
#input price "p", look-back window "window",
#signal type = buy only (default) --gives long signals, sell only --gives sell signals, both --gives both long and short signals
#return a list of signals = 1 for long position and -1 for short position
signals = np.zeros(len(p))
if len(p)<window:
#no signal if no sufficient data
return signals
sma = list(np.zeros(window)+np.nan) #the first few prices does not give technical indicator values
sma += [np.average(p[k:k+window]) for k in np.arange(len(p)-window)]
for i in np.arange(len(p)-1):
if np.isnan(sma[i]):
continue #skip the open market time window
if sma[i]<p[i] and (signal_type=='buy only' or signal_type=='both'):
signals[i] = 1
elif sma[i]>p[i] and (signal_type=='sell only' or signal_type=='both'):
signals[i] = -1
return signals

I have two solutions to this. One is to loop through each group, then add the relevant data to the summary_table, the other is to calculate the whole series and set the RSI column as this.
I first recreated the data:
import yfinance
import pandas as pd
# initially created similar data through yfinance,
# then copied this to Excel and changed the Datetime column to match yours.
df = yfinance.download("AAPL", period="60d", interval="5m")
# copied it and read it as a dataframe
df = pd.read_clipboard(sep=r'\s{2,}', engine="python")
df.head()
# Datetime Open High Low Close Adj Close Volume
#0 2022-03-03 09:30-05:00 168.470001 168.910004 167.970001 168.199905 168.199905 5374241
#1 2022-03-03 09:35-05:00 168.199997 168.289993 167.550003 168.129898 168.129898 1936734
#2 2022-03-03 09:40-05:00 168.119995 168.250000 167.740005 167.770004 167.770004 1198687
#3 2022-03-03 09:45-05:00 167.770004 168.339996 167.589996 167.718094 167.718094 2128957
#4 2022-03-03 09:50-05:00 167.729996 167.970001 167.619995 167.710007 167.710007 968410
Then I formatted the data and created the summary_table:
df["date"] = pd.to_datetime(df["Datetime"].str[:16], format="%Y-%m-%d %H:%M").dt.date
# calculate percentage change from open and close of each row
df["gain"] = (df["Close"] / df["Open"]) - 1
# your summary table, slightly changing the index to use the dates above
cols = ['Num. Obs.', 'Num. Trade', 'PnL', 'Win. Ratio','RSI'] #add addtional fields if necessary
summary_table = pd.DataFrame(index=df["date"].unique(), columns=cols)
Option 1:
# loop through each group, calculate the average gain and loss, then RSI
for grp, data in df.groupby("date"):
# average gain for gain greater than 0
average_gain = data[data["gain"] > 0]["gain"].mean()
# average loss for gain less than 0
average_loss = data[data["gain"] < 0]["gain"].mean()
# add to relevant cell of summary_table
summary_table["RSI"].loc[grp] = 100 - (100 / (1 + (average_gain / average_loss)))
Option 2:
# define a function to apply in the groupby
def rsi_calc(series):
avg_gain = series[series > 0].mean()
avg_loss = series[series < 0].mean()
return 100 - (100 / (1 + (avg_gain / avg_loss)))
summary_table["RSI"] = df.groupby("date")["gain"].apply(lambda x: rsi_calc(x))
Output (same for each):
summary_table.head()
# Num. Obs. Num. Trade PnL Win. Ratio RSI
#2022-03-03 NaN NaN NaN NaN -981.214015
#2022-03-04 NaN NaN NaN NaN 501.950956
#2022-03-07 NaN NaN NaN NaN -228.379066
#2022-03-08 NaN NaN NaN NaN -2304.451654
#2022-03-09 NaN NaN NaN NaN -689.824739

Background:I have a DataFrame ('weather_tweets') containing two columns of interest, weather (weather on the planet Mars) and date (the date the weather relates). Structure as follows:
Objective:I am trying to write code that will determine the latest datestamp (date column) and print that row's corresponding weather column value.Sample rows:Here is a sample row:
weather_tweets = [
('tweet', 'weather', 'date'),
('Mars Weather#MarsWxReport·Jul 15InSight sol 58', 'InSight sol 580 (2020-07-14) low -88.8ºC (-127.8ºF) high -8.4ºC (16.8ºF) winds from the WNW at 5.9 m/s (13.3 mph) gusting to 15.4 m/s (34.4 mph) pressure at 7.80 hPa, '2020-07-14')]
My code:Thus far, I have only been able to formulate some messy code that will return the latest dates in order, but it's pretty useless for my expected results:latest_weather = weather_tweets.groupby(['tweet', 'weather'])['date'].transform(max) == weather_tweets['date']print(weather_tweets[latest_weather])
Any advice on how to reach the desired result would be much appreciated.

Try:
weather_tweets[weather_tweets.date == weather_tweets.date.max()].weather
You can add to_frame() at the end to obtain more elegant dataframe result:
weather_tweets[weather_tweets.date == weather_tweets.date.max()].weather.to_frame()
Or create new dataframe:
df_latest = weather_tweets.loc[weather_tweets.date == weather_tweets.date.max(),['weather','date']]
df_max.columns = ['latest_weather','latest_date']

I have a crime data set, I already calculated the crimes committed in each location. Now I want to create a new column that is the crime rate for that specific row. I already calculated the crime rate now I want to match the specific crime rate to correct row matching the same latitude value
Here I have a loop that creates the crime rate per location. but now i want to get the crime rate value create a new column that matches the latitude in my for loop with my dataframe and adds to corresponding crime rate in each individual row
z = ['lat']
for i in z:
print((df[i].value_counts()/1250000)*100000)
32.715973 112.56
32.715738 90.32
32.706341 83.28
32.545300 79.52
32.745903 78.32
32.769389 65.52
32.809860 63.44
32.706287 63.04
32.591684 55.68
32.764136 55.44
32.749983 52.16
32.545291 49.04
32.712584 47.20
32.746868 46.32
32.796864 44.40
32.706287 43.76
32.768120 42.64
32.794497 41.52
32.703369 40.80
32.714797 40.40
32.716977 39.44
32.738989 39.04
32.755182 37.28
32.957955 35.52
32.759375 35.28
32.565237 34.72
32.739964 34.08
32.767116 34.00
32.877050 32.24
32.706559 32.24

I think you can using transform
df['Newcol']=(df.groupby('lat')['lat'].transform('count')/1250000)*100000
For loop
z = ['lat']
for i in z:
df[i+'col']=(df.groupby(i)[i].transform('count')/1250000)*100000

I have a dataframe :
CAT ^GSPC
Date
2012-01-06 80.435059 1277.810059
2012-01-09 81.560600 1280.699951
2012-01-10 83.962914 1292.079956
....
2017-09-16 144.56653 2230.567646
and I want to find the slope of the stock / and S&P index for the last 63 days for each period. I have tried :
x = 0
temp_dct = {}
for date in df.index:
x += 1
max(x, (len(df.index)-64))
temp_dct[str(date)] = np.polyfit(df['^GSPC'][0+x:63+x].values,
df['CAT'][0+x:63+x].values,
1)[0]
However I feel this is very "unpythonic" , but I've had trouble integrating rolling/shift functions into this.
My expected output is to have a column called "Beta" that has the slope of the S&P (x values) and stock (y values) for all dates available

# this will operate on series
def polyf(seri):
return np.polyfit(seri.index.values, seri.values, 1)[0]
# you can store the original index in a column in case you need to reset back to it after fitting
df.index = df['^GSPC']
df['slope'] = df['CAT'].rolling(63, min_periods=2).apply(polyf, raw=False)
After running this, there will be a new column store the fitting result.

I have a dataset with weights for each observation and I want to prepare weighted summaries using groupby but am rusty as to how to best do this. I think it implies a custom aggregation function. My issue is how to properly deal with not item-wise data, but group-wise data. Perhaps it means that it is best to do this in steps rather than in one go.
In pseudo-code, I am looking for
#first, calculate weighted value
for each row:
weighted jobs = weight * jobs
#then, for each city, sum these weights and divide by the count (sum of weights)
for each city:
sum(weighted jobs)/sum(weight)
I am not sure how to work the "for each city"-part into a custom aggregate function and get access to group-level summaries.
Mock data:
import pandas as pd
import numpy as np
np.random.seed(43)
## prep mock data
N = 100
industry = ['utilities','sales','real estate','finance']
city = ['sf','san mateo','oakland']
weight = np.random.randint(low=5,high=40,size=N)
jobs = np.random.randint(low=1,high=20,size=N)
ind = np.random.choice(industry, N)
cty = np.random.choice(city, N)
df_city =pd.DataFrame({'industry':ind,'city':cty,'weight':weight,'jobs':jobs})

Simply multiply the two columns:
In [11]: df_city['weighted_jobs'] = df_city['weight'] * df_city['jobs']
Now you can groupby the city (and take the sum):
In [12]: df_city_sums = df_city.groupby('city').sum()
In [13]: df_city_sums
Out[13]:
jobs weight weighted_jobs
city
oakland 362 690 7958
san mateo 367 1017 9026
sf 253 638 6209
[3 rows x 3 columns]
Now you can divide the two sums, to get the desired result:
In [14]: df_city_sums['weighted_jobs'] / df_city_sums['jobs']
Out[14]:
city
oakland 21.983425
san mateo 24.594005
sf 24.541502
dtype: float64