Develop Reference

How to Merge a list of Multiple DataFrames and Tag each Column with a another list

I have a lisit of DataFrames that come from the census api, i had stored each year pull into a list.
So at the end of my for loop i have a list with dataframes per year and a list of years to go along side the for loop.
The problem i am having is merging all the DataFrames in the list while also taging them with a list of years.
So i have tried using the reduce function, but it looks like it only taking 2 of the 6 Dataframes i have.
concat just adds them to the dataframe with out tagging or changing anything
# Dependencies
import pandas as pd
import requests
import json
import pprint
import requests
from census import Census
from us import states
# Census
from config import (api_key, gkey)
year = 2012
c = Census(api_key, year)
for length in range(6):
c = Census(api_key, year)
data = c.acs5.get(('NAME', "B25077_001E","B25064_001E",
"B15003_022E","B19013_001E"),
{'for': 'zip code tabulation area:*'})
data_df = pd.DataFrame(data)
data_df = data_df.rename(columns={"NAME": "Name",
"zip code tabulation area": "Zipcode",
"B25077_001E":"Median Home Value",
"B25064_001E":"Median Rent",
"B15003_022E":"Bachelor Degrees",
"B19013_001E":"Median Income"})
data_df = data_df.astype({'Zipcode':'int64'})
filtervalue = data_df['Median Home Value']>0
filtervalue2 = data_df['Median Rent']>0
filtervalue3 = data_df['Median Income']>0
cleandata = data_df[filtervalue][filtervalue2][filtervalue3]
cleandata = cleandata.dropna()
yearlst.append(year)
datalst.append(cleandata)
year += 1
so this generates the two seperate list one with the year and other with dataframe.
So my output came out to either one Dataframe with missing Dataframe entries or it just concatinated all without changing columns.
what im looking for is how to merge all within a list, but datalst[0] to be tagged with yearlst[0] when merging if at all possible

No need for year list, simply assign year column to data frame. Plus avoid incrementing year and have it the iterator column. In fact, consider chaining your process:
for year in range(2012, 2019):
c = Census(api_key, year)
data = c.acs5.get(('NAME', "B25077_001E","B25064_001E", "B15003_022E","B19013_001E"),
{'for': 'zip code tabulation area:*'})
cleandata = (pd.DataFrame(data)
.rename(columns={"NAME": "Name",
"zip code tabulation area": "Zipcode",
"B25077_001E": "Median_Home_Value",
"B25064_001E": "Median_Rent",
"B15003_022E": "Bachelor_Degrees",
"B19013_001E": "Median_Income"})
.astype({'Zipcode':'int64'})
.query('(Median_Home_Value > 0) & (Median_Rent > 0) & (Median_Income > 0)')
.dropna()
.assign(year_column = year)
)
datalst.append(cleandata)
final_data = pd.concat(datalst, ignore_index = True)

Merge Data Frames By Date With Unequal Dates

My process is this:
Import csv of data containing dates, activations, and cancellations
subset the data by activated or cancelled
pivot the data with aggfunc 'sum'
convert back to data frames
Now, I need to merge the 2 data frames together but there are dates that exist in one data frame but not the other. Both data frames start Jan 1, 2017 and end Dec 31, 2017. Preferably, the output for any observation in which the index month needs to be filled with have a corresponding value of 0.
Here's the .head() from both data frames:
For reference, here's the code up to this point:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import datetime
%matplotlib inline
#import data
directory1 = "C:\python\Contracts"
directory_source = os.path.join(directory1, "Contract_Data.csv")
df_source = pd.read_csv(directory_source)
#format date ranges as times
#df_source["Activation_Month"] = pd.to_datetime(df_source["Activation_Month"])
#df_source["Cancellation_Month"] = pd.to_datetime(df_source["Cancellation_Month"])
df_source["Activation_Day"] = pd.to_datetime(df_source["Activation_Day"])
df_source["Cancellation_Day"] = pd.to_datetime(df_source["Cancellation_Day"])
#subset the data based on status
df_active = df_source[df_source["Order Status"]=="Active"]
df_active = pd.DataFrame(df_active[["Activation_Day", "Event_Value"]].copy())
df_cancelled = df_source[df_source["Order Status"]=="Cancelled"]
df_cancelled = pd.DataFrame(df_cancelled[["Cancellation_Day", "Event_Value"]].copy())
#remove activations outside 2017 and cancellations outside 2017
df_cancelled = df_cancelled[(df_cancelled['Cancellation_Day'] > '2016-12-31') &
(df_cancelled['Cancellation_Day'] <= '2017-12-31')]
df_active = df_active[(df_active['Activation_Day'] > '2016-12-31') &
(df_active['Activation_Day'] <= '2017-12-31')]
#pivot the data to aggregate by day
df_active_aggregated = df_active.pivot_table(index='Activation_Day',
values='Event_Value',
aggfunc='sum')
df_cancelled_aggregated = df_cancelled.pivot_table(index='Cancellation_Day',
values='Event_Value',
aggfunc='sum')
#convert pivot tables back to useable dataframes
activations_aggregated = pd.DataFrame(df_active_aggregated.to_records())
cancellations_aggregated = pd.DataFrame(df_cancelled_aggregated.to_records())
#rename the time columns so they can be referenced when merging into one DF
activations_aggregated.columns = ["index_month", "Activations"]
#activations_aggregated = activations_aggregated.set_index(pd.DatetimeIndex(activations_aggregated["index_month"]))
cancellations_aggregated.columns = ["index_month", "Cancellations"]
#cancellations_aggregated = cancellations_aggregated.set_index(pd.DatetimeIndex(cancellations_aggregated["index_month"]))
I'm aware there are many posts that address issues similar to this but I haven't been able to find anything that has helped. Thanks to anyone that can give me a hand with this!

You can try:
activations_aggregated.merge(cancellations_aggregated, how='outer', on='index_month').fillna(0)

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

Pandas: fastest way to the DF by date

I have an efficiency question for you. I wrote some code to analyze a report that holds over 70k records and over 400+ unique organizations to allow my supervisor to enter in year/month/date they are interested in and have it pop out the information.
The beginning of my code is:
import pandas as pd
import numpy as np
import datetime
main_data = pd.read_excel("UpdatedData.xlsx", encoding= 'utf8')
#column names from DF
epi_expose = "EpitheliumExposureSeverity"
sloughing = "EpitheliumSloughingPercentageSurface"
organization = "OrgName"
region = "Region"
date = "DeathOn"
#list storage of definitions
sl_list = ["",'None','Mild','Mild to Moderate']
epi_list= ['Moderate','Moderate to Severe','Severe']
#Create DF with four columns
df = main_data[[region, organization, epi_expose, sloughing, date]]
#filter it down to months
starting_date = datetime.date(2017,2,1)
ending_date = datetime.date(2017,2,28)
df = df[(df[date] > starting_date) & (df[date] < ending_date)]
I am then performing conditional filtering below to get counts by region and organization. It works, but is slow. Is there a more efficient way to query my DF and set up a DF that ONLY has the dates that it is supposed to sit between? Or is this the most efficient way without altering how the Database I am using is set up?
I can provide more of my code but if I filter it out by month before exporting to excel, the code runs in a matter of seconds so I am not concerned about the speed of it besides getting the correct date fields.
Thank you!

Python: store a value in a variable so that you can recognize each reoccurence

If this question is unclear, I am very open to constructive criticism.
I have an excel table with about 50 rows of data, with the first column in each row being a date. I need to access all the data for only one date, and that date appears only about 1-5 times. It is the most recent date so I've already organized the table by date with the most recent being at the top.
So my goal is to store that date in a variable and then have Python look only for that variable (that date) and take only the columns corresponding to that variable. I need to use this code on 100's of other excel files as well, so it would need to arbitrarily take the most recent date (always at the top though).
My current code below simply takes the first 5 rows because I know that's how many times this date occurs.
import os
from numpy import genfromtxt
import pandas as pd
path = 'Z:\\folderwithcsvfile'
for filename in os.listdir(path):
file_path = os.path.join(path, filename)
if os.path.isfile(file_path):
broken_df = pd.read_csv(file_path)
df3 = broken_df['DATE']
df4 = broken_df['TRADE ID']
df5 = broken_df['AVAILABLE STOCK']
df6 = broken_df['AMOUNT']
df7 = broken_df['SALE PRICE']
print (df3)
#print (df3.head(6))
print (df4.head(6))
print (df5.head(6))
print (df6.head(6))
print (df7.head(6))

This is a relatively simple filtering operation. You state that you want to "take only the columns" that are the latest date, so I assume that an acceptable result will be a filter DataFrame with just the correct columns.
Here's a simple CSV that is similar to your structure:
DATE,TRADE ID,AVAILABLE STOCK
10/11/2016,123,123
10/11/2016,123,123
10/10/2016,123,123
10/9/2016,123,123
10/11/2016,123,123
Note that I mixed up the dates a little bit, because it's hacky and error-prone to just assume that the latest dates will be on the top. The following script will filter it appropriately:
import pandas as pd
import numpy as np
df = pd.read_csv('data.csv')
# convert the DATE column to datetimes
df['DATE'] = pd.to_datetime(df['DATE'])
# find the latest datetime
latest_date = df['DATE'].max()
# use index filtering to only choose the columns that equal the latest date
latest_rows = df[df['DATE'] == latest_date]
print (latest_rows)
# now you can perform your operations on latest_rows
In my example, this will print:
DATE TRADE ID AVAILABLE STOCK
0 2016-10-11 123 123
1 2016-10-11 123 123
4 2016-10-11 123 123