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Using two columns in an existing SQLite database to create a third column using Python

I have created a database with multiple columns and am wanting to use the data stored in two of the columns (named 'cost' and 'Mwe') to create a new column 'Dollar_per_KWh'. I have created two lists, one contains the rowid and the other contains the new value that I want to populate the new Dollar_per_KWh column. As it iterates through all the rows, the two lists are zipped together into a dictionary containing tuples. I then try to populate the new sqlite column. The code runs and I do not receive any errors. When I print out the dictionary it looks correct.
Issue: the new column in my database is not being updated with the new data and I am not sure why. The values in the new column are showing 'NULL'
Thank you for your help. Here is my code:
conn = sqlite3.connect('nuclear_builds.sqlite')
cur = conn.cursor()
cur.execute('''ALTER TABLE Construction
ADD COLUMN Dollar_per_KWh INTEGER''')
cur.execute('SELECT _rowid_, cost, Mwe FROM Construction')
data = cur.fetchall()
dol_pr_kW = dict()
key = list()
value = list()
for row in data:
id = row[0]
cost = row[1]
MWe = row[2]
value.append(int((cost*10**6)/(MWe*10**3)))
key.append(id)
dol_pr_kW = list(zip(key, value))
cur.executemany('''UPDATE Construction SET Dollar_per_KWh = ? WHERE _rowid_ = ?''', (dol_pr_kW[1], dol_pr_kW[0]))
conn.commit()

Not sure why it isn't working. Have you tried just doing it all in SQL?
conn = sqlite3.connect('nuclear_builds.sqlite')
cur = conn.cursor()
cur.execute('''ALTER TABLE Construction
ADD COLUMN Dollar_per_KWh INTEGER;''')
cur.execute('''UPDATE Construction SET Dollar_per_KWh = cast((cost/MWe)*1000 as integer);''')
It's a lot simpler just doing the calculation in SQL than pulling data to Python, manipulating it, and pushing it back to the database.
If you need to do this in Python for some reason, testing whether this works will at least give you some hints as to what is going wrong with your current code.
Update: I see a few more problems now.
First I see you are creating an empty dictionary dol_pr_kW before the for loop. This isn't necessary as you are re-defining it as a list later anyway.
Then you are trying to create the list dol_pr_kW inside the for loop. This has the effect of over-writing it for each row in data.
I'll give a few different ways to solve it. It looks like you were trying a few different things at once (using dict and list, building two lists and zipping into a third list, etc.) that is adding to your trouble, so I am simplifying the code to make it easier to understand. In each solution I will create a list called data_to_insert. That is what you will pass at the end to the executemany function.
First option is to create your list before the for loop, then append it for each row.
dol_pr_kW = list()
for row in data:
id = row[0]
cost = row[1]
MWe = row[2]
val = int((cost*10**6)/(MWe*10**3))
dol_pr_kW.append(id,val)
#you can do this or instead change above step to dol_pr_kW.append(val,id).
data_to_insert = [(r[1],r[0]) for r in dol_pr_kW]
The second way would be to zip the key and value lists AFTER the for loop.
key = list()
value = list()
for row in data:
id = row[0]
cost = row[1]
MWe = row[2]
value.append(int((cost*10**6)/(MWe*10**3)))
key.append(id)
dol_pr_kW = list(zip(key,value))
#you can do this or instead change above step to dol_pr_kW=list(zip(value,key))
data_to_insert = [(r[1],r[0]) for r in dol_pr_kW]
Third, if you would rather keep it as an actual dict you can do this.
dol_pr_kW = dict()
for row in data:
id = row[0]
cost = row[1]
MWe = row[2]
val = int((cost*10**6)/(MWe*10**3))
dol_pr_kW[id] = val
# convert to list
data_to_insert = [(dol_pr_kW[id], id) for id in dol_per_kW]
Then to execute call
cur.executemany('''UPDATE Construction SET Dollar_per_KWh = ? WHERE _rowid_ = ?''', data_to_insert)
cur.commit()
I prefer the first option since it's easiest for me to understand what's happening at a glance. Each iteration of the for loop just adds a (id, val) to the end of the list. It's a little more cumbersome to build two lists independently and zip them together to get a third list.
Also note that if the dol_pr_kW list had been created correctly, passing (dol_pr_kW[1],dol_pr_kW[0]) to executemany would pass the first two rows in the list instead of reversing (key,value) to (value,key). You need to do a list comprehension to accomplish the swap in one line of code. I just did this as a separate line and assigned it to variable data_to_insert for readability.

python sqlite: write a big list to sqlite database

i am new to sqlite and i think this question should have been answered before but i havent been able to find an answer.
i have a list of around 50 elements that i need to write to an sqlite database with 50 columns.
went over the documentation # https://docs.python.org/2/library/sqlite3.html but in the examples the values are specified by ? (so for writing 3 values, 3 ? are specified
sample code:
row_to_write = range(50)
conn = sqlite3.connect('C:\sample_database\sample_database')
c = conn.cursor()
tried these:
approach 1
c.execute("INSERT INTO PMU VALUES (?)", row_to_write)
ERROR: OperationalError: table PMU has 50 columns but 1 values were supplied
approach 2...tried writing a generator for iterating over list
def write_row_value_generator(row_to_write):
for val in row_to_write:
yield (val,)
c.executemany("INSERT INTO PMU VALUES (?)", write_row_value_generator(row_to_write))
ERROR: OperationalError: table PMU has 50 columns but 1 values were supplied
What is the correct way of doing this?

Assuming that your row_to_write has exactly the same number of items as PMU has columns, you can create a string of ? marks easily using str.join : ','.join(['?']*len(row_to_write))
import sqlite3
conn = sqlite3.connect(':memory:')
c = conn.cursor()
c.execute("create table PMU (%s)" % ','.join("col%d"%i for i in range(50)))
row_to_write = list(range(100,150,1))
row_value_markers = ','.join(['?']*len(row_to_write))
c.execute("INSERT INTO PMU VALUES (%s)"%row_value_markers, row_to_write)
conn.commit()

You need to specify the names of the columns. Sqlite will not guess those for you.
columns = ['A', 'B', 'C', ...]
n = len(row_to_write)
sql = "INSERT INTO PMU {} VALUES ({})".format(
', '.join(columns[:n]) , ', '.join(['?']*n))
c.execute(sql, row_to_write)
Note also that if your rows have a variable number of columns, then you might want to rethink your database schema. Usually each row should have a fixed number of columns, and the variability expresses itself in the number of rows inserted, not the number of columns used.
For example, instead of having 50 columns, perhaps you need just one extra column, whose value is one of 50 names (what used to be a column name). Each value in row_to_write would have its own row, and for each row you would have two columns: the value and the name of the column.

Add MySQL query results to R dataframe

I want to convert a MySQL query from a python script to an analogous query in R. The python uses a loop structure to search for specific values using genomic coordinates:
SQL = """SELECT value FROM %s FORCE INDEX (chrs) FORCE INDEX (sites)
WHERE `chrom` = %d AND `site` = %d""" % (Table, Chr, Start)
cur.execute(SQL)
In R the chromosomes and sites are in a dataframe and for every row in the dataframe I would like to extract a single value and add it to a new column in the dataframe
So my current dataframe has a similar structure to the following:
df <- data.frame("Chr"=c(1,1,3,5,5), "Site"=c(100, 200, 400, 100, 300))
The amended dataframe should have an additional column with values from the database (at corresponding genomic coordinates. The structure should be similar to:
df <- data.frame("Chr"=c(1,1,3,5,5), "Site"=c(100, 200, 400, 100, 300), "Value"=c(1.5, 0, 5, 60, 100)
So far I connected to the database using:
con <- dbConnect(MySQL(),
user="root", password="",
dbname="MyDataBase")
Rather than loop over each row in my dataframe, I would like to use something that would add the corresponding value to a new column in the existing dataframe.
Update with working solution based on answer below:
library(RMySQL)
con <- dbConnect(MySQL(),
user="root", password="",
dbname="MyDataBase")
GetValue <- function(DataFrame, Table){
queries <- sprintf("SELECT value as value
FROM %s FORCE INDEX (chrs) FORCE INDEX (sites)
WHERE chrom = %d AND site = %d UNION ALL SELECT 'NA' LIMIT 1", Table, DataFrame$Chr, DataFrame$start)
res <- ldply(queries, function(query) { dbGetQuery(con, query)})
DataFrame[, Table] <- res$value
return(DataFrame)
}
df <- GetValue(df, "TableName")

Maybe you could do something like this. First, build up your queries, then execute them, storing the results in a column of your dataframe. Not sure if the do.call(rbind part is necessary, but that basically takes a bunch of dataframe rows, and squishes them together by row into a dataframe.
queries=sprintf("SELECT value as value FROM %s FORCE INDEX (chrs) FORCE INDEX (sites) WHERE chrom = %d AND site = %d UNION ALL SELECT 0 LIMIT 1", "TableName", df$Chrom, df$Pos)
df$Value = do.call("rbind",sapply(queries, function(query) dbSendQuery(mydb, query)))$value
I played with your SQL a little, my concern with the original is with cases where it might return more than 1 row.

I like the data.table package for this kind of tasks as its syntax is inspired by SQL
require(data.table)
So an example database to match the values to a table
table <- data.table(chrom=rep(1:5, each=5),
site=rep(100*1:5, times=5),
Value=runif(5*5))
Now the SQL query can be translated into something like
# select from table, where chrom=Chr and site=Site, value
Chr <- 2
Site <- 200
table[chrom==Chr & site==Site, Value] # returns data.table
table[chrom==Chr & site==Site, ]$Value # returns numeric
Key (index) database for quick lookup (assuming unique chrom and site..)
setkey(table, chrom, site)
table[J(Chr, Site), ]$Value # very fast lookup due to indexed table
Your dataframe as data table with two columns 'Chr' and 'Site' both integer
df <- data.frame("Chr"=c(1,1,3,5,5), "Site"=c(100, 200, 400, 100, 300))
dt <- as.data.table(df) # adds data.table class to data.frame
setkey(dt, Chr, Site) # index for 'by' and for 'J' join
Match the values and append in new column (by reference, so no copying of table)
# loop over keys Chr and Site and find the match in the table
# select the Value column and create a new column that contains this
dt[, Value:=table[chrom==Chr & site==Site]$Value, by=list(Chr, Site)]
# faster:
dt[, Value:=table[J(Chr, Site)]$Value, by=list(Chr, Site)]
# fastest: in one table merge operation assuming the keys are in the same order
table[J(dt)]
kind greetings

Why don't you use the RMySQL or sqldf package?
With RMySQL, you get MySQL access in R.
With sqldf, you can issue SQL queries on R data structures.
Using either of those, you do not need to reword you SQL query to get the same results.
Let me also mention the data.table package, which lets you do very efficient selects and joins on your data frames after converting them to data tables using as.data.table(your.data.frame). Another good thing about it is that a data.table object is a data.frame at the same time, so all your functions that work on the data frames work on these converted objects, too.

You could easily use dplyr package. There is even nice vignette about that - http://cran.rstudio.com/web/packages/dplyr/vignettes/databases.html.
One thing you need to know is:
You can connect to MySQL and MariaDB (a recent fork of MySQL) through
src_mysql(), mediated by the RMySQL package. Like PostgreSQL, you'll
need to provide a dbname, username, password, host, and port.

Obtaining data from PostgreSQL as Dictionary

I have a database table with multiple fields which I am querying and pulling out all data which meets certain parameters. I am using psycopg2 for python with the following syntax:
cur.execute("SELECT * FROM failed_inserts where insertid='%s' AND site_failure=True"%import_id)
failed_sites= cur.fetchall()
This returns the correct values as a list with the data's integrity and order maintained. However I want to query the list returned somewhere else in my application and I only have this list of values, i.e. it is not a dictionary with the fields as the keys for these values. Rather than having to do
desiredValue = failed_sites[13] //where 13 is an arbitrary number of the index for desiredValue
I want to be able to query by the field name like:
desiredValue = failed_sites[fieldName] //where fieldName is the name of the field I am looking for
Is there a simple way and efficient way to do this?
Thank you!

cursor.description will give your the column information (http://www.python.org/dev/peps/pep-0249/#cursor-objects). You can get the column names from it and use them to create a dictionary.
cursor.execute('SELECT ...')
columns = []
for column in cursor.description:
columns.append(column[0].lower())
failed_sites = {}
for row in cursor:
for i in range(len(row)):
failed_sites[columns[i]] = row[i]
if isinstance(row[i], basestring):
failed_sites[columns[i]] = row[i].strip()

The "Dictionary-like cursor", part of psycopg2.extras, seems what you're looking for.

Dynamically parsing research data in python

The long (winded) version:
I'm gathering research data using Python. My initial parsing is ugly (but functional) code which gives me some basic information and turns my raw data into a format suitable for heavy duty statistical analysis using SPSS. However, every time I modify the experiment, I have to dive into the analysis code.
For a typical experiment, I'll have 30 files, each for a unique user. Field count is fixed for each experiment (but can vary from one to another 10-20). Files are typically 700-1000 records long with a header row. Record format is tab separated (see sample which is 4 integers, 3 strings, and 10 floats).
I need to sort my list into categories. In a 1000 line file, I could have 4-256 categories. Rather than trying to pre-determine how many categories each file has, I'm using the code below to count them. The integers at the beginning of each line dictate what category the float values in the row correspond to. Integer combinations can be modified by the string values to produce wildly different results, and multiple combinations can sometimes be lumped together.
Once they're in categories, number crunching begins. I get statistical info (mean, sd, etc. for each category for each file).
The essentials:
I need to parse data like the sample below into categories. Categories are combos of the non-floats in each record. I'm also trying to come up with a dynamic (graphical) way to associate column combinations with categories. Will make a new post fot this.
I'm looking for suggestions on how to do both.
# data is a list of tab separated records
# fields is a list of my field names
# get a list of fieldtypes via gettype on our first row
# gettype is a function to get type from string without changing data
fieldtype = [gettype(n) for n in data[1].split('\t')]
# get the indexes for fields that aren't floats
mask = [i for i, field in enumerate(fieldtype) if field!="float"]
# for each row of data[skipping first and last empty lists] we split(on tabs)
# and take the ith element of that split where i is taken from the list mask
# which tells us which fields are not floats
records = [[row.split('\t')[i] for i in mask] for row in data[1:-1]]
# we now get a unique set of combos
# since set doesn't happily take a list of lists, we join each row of values
# together in a comma seperated string. So we end up with a list of strings.
uniquerecs = set([",".join(row) for row in records])
print len(uniquerecs)
quit()
def gettype(s):
try:
int(s)
return "int"
except ValueError:
pass
try:
float(s)
return "float"
except ValueError:
return "string"
Sample Data:
field0 field1 field2 field3 field4 field5 field6 field7 field8 field9 field10 field11 field12 field13 field14 field15
10 0 2 1 Right Right Right 5.76765674196 0.0310912272139 0.0573603238282 0.0582901376612 0.0648936500524 0.0655294305058 0.0720571099855 0.0748289246137 0.446033755751
3 1 3 0 Left Left Right 8.00982745764 0.0313840132052 0.0576521406854 0.0585844966069 0.0644905497442 0.0653386429438 0.0712603578765 0.0740345755708 0.2641076191
5 19 1 0 Right Left Left 4.69440026591 0.0313852052224 0.0583165354345 0.0592403274967 0.0659404609478 0.0666070804916 0.0715314027001 0.0743022054775 0.465994962101
3 1 4 2 Left Right Left 9.58648184552 0.0303649003017 0.0571579895338 0.0580911765412 0.0634304670863 0.0640132919609 0.0702920967445 0.0730697946335 0.556525293
9 0 0 7 Left Left Left 7.65374257547 0.030318719717 0.0568551744109 0.0577785415066 0.0640577002605 0.0647226582655 0.0711459854908 0.0739256050784 1.23421547397

Not sure if I understand your question, but here are a few thoughts:
For parsing the data files, you usually use the Python csv module.
For categorizing the data you could use a defaultdict with the non-float fields joined as a key for the dict. Example:
from collections import defaultdict
import csv
reader = csv.reader(open('data.file', 'rb'), delimiter='\t')
data_of_category = defaultdict(list)
lines = [line for line in reader]
mask = [i for i, n in enumerate(lines[1]) if gettype(n)!="float"]
for line in lines[1:]:
category = ','.join([line[i] for i in mask])
data_of_category[category].append(line)
This way you don't have to calculate the categories in the first place an can process the data in one pass.
And I didn't understand the part about "a dynamic (graphical) way to associate column combinations with categories".

For at least part of your question, have a look at Named Tuples

Step 1: Use something like csv.DictReader to turn the text file into an iterable of rows.
Step 2: Turn that into a dict of first entry: rest of entries.
with open("...", "rb") as data_file:
lines = csv.Reader(data_file, some_custom_dialect)
categories = {line[0]: line[1:] for line in lines}
Step 3: Iterate over the items() of the data and do something with each line.
for category, line in categories.items():
do_stats_to_line(line)

Some useful answers already but I'll throw mine in as well. Key points:
Use the csv module
Use collections.namedtuple for each row
Group the rows using a tuple of int field values as the key
If your source rows are sorted by the keys (the integer column values), you could use itertools.groupby. This would likely reduce memory consumption. Given your example data, and the fact that your files contain >= 1000 rows, this is probably not an issue to worry about.
def coerce_to_type(value):
_types = (int, float)
for _type in _types:
try:
return _type(value)
except ValueError:
continue
return value
def parse_row(row):
return [coerce_to_type(field) for field in row]
with open(datafile) as srcfile:
data = csv.reader(srcfile, delimiter='\t')
## Read headers, create namedtuple
headers = srcfile.next().strip().split('\t')
datarow = namedtuple('datarow', headers)
## Wrap with parser and namedtuple
data = (parse_row(row) for row in data)
data = (datarow(*row) for row in data)
## Group by the leading integer columns
grouped_rows = defaultdict(list)
for row in data:
integer_fields = [field for field in row if isinstance(field, int)]
grouped_rows[tuple(integer_fields)].append(row)
## DO SOMETHING INTERESTING WITH THE GROUPS
import pprint
pprint.pprint(dict(grouped_rows))
EDIT You may find the code at https://gist.github.com/985882 useful.