I have a csv file with data on grouping of cereal brands in an experiment. I have one row for each subject (~2000 subjects) and each row has a variable number of brand that people liked (text strings separated by commas)
srno Brands
1 GMI 'TOTAL' WG ORIG,POS H-C GRAPE NUTS ORIG,POST GREAT GRAINS CRUNCHY PCN
2 GMI TINY TST STB,GMI HONEY NUT CHEERIOS REG,GMI TINY TST BB,GMI APPLE CN TOAST CRUNCH
3 QKR SQUARES CN
I want to read the data into a data frame so that I have the brands in each row as one element of a list
My goal is to be able to do a text mining analysis to explore similarities (ie brands that occur together)
I see a similar question asked a few years ago but I was not able to adapt the solution
Text file to list in R
Managed to work this out!
I read in the csv file with StringsAsFactors=FALSE option (this is important)
`x = read.csv ("datafile.csv", stringsAsFactors=FALSE)
#strings of brand names read into variable str_brand
#the following stmt then turns the chars in str_brand into a list
#note..I had the individual brands separated by commas in the csv file
brands_list <- stringr::str_split(x$str_brand,pattern = ",")
`
Related
I need help solving this problem:
I have a directory full of .txt files that look like this:
file1.no
file2.no
file3.no
And every file has the following structure (I only care for the first two "columns" in the .txt):
#POS SEQ SCORE QQ-INTERVAL STD MSA DATA
#The alpha parameter 0.75858
#The likelihood of the data given alpha and the tree is:
#LL=-4797.62
1 M 0.3821 [0.01331,0.5465] 0.4421 7/7
2 E 0.4508 [0.05393,0.6788] 0.5331 7/7
3 L 0.5334 [0.05393,0.6788] 0.6279 7/7
4 G 0.5339 [0.05393,0.6788] 0.624 7/7
And I want to parse all of them into one DataFrame, while also converting the columns into lists for each row (i.e., the first column should be converted into a string like this: ["MELG"]).
But now I am running into two issues:
How to read the different files and append all of them to a single DataFrame, and also making a single column out of al the rows inside said files
How to parse this files, giving that the spaces between the columns vary for almost all of them.
My output should look like this:
|File |SEQ |SCORE|
| --- | ---| --- |
|File1|MELG|0.3821,0.4508,0.5334,0.5339|
|File2|AAHG|0.5412,1,2345,0.0241,0.5901|
|File3|LLKM|0.9812,0,2145,0.4142,0.4921|
So, the first column for the first file (file1.no), the one with single letters, is now in a list, in a row with all the information from that file, and the DataFrame has one row for each file.
Any help is welcome, thanks in advance.
Here is an example code that should work for you:
using DataFrames
function parsefile(filename)
l = readlines(filename)
filter!(x -> !startswith(x, "#"), l)
sl = split.(l)
return (File=filename,
SEQ=join(getindex.(sl, 2)),
SCORE=parse.(Float64, getindex.(sl, 3)))
end
df = DataFrame()
foreach(fn -> push!(df, parsefile(fn)), ["file$i.no" for i in 1:3])
your result will be in df data frame.
I have a dataset. I am using pandas dataframe and named it df.
The dataset has 50,000 rows - here are the first 5:.
Name_Restaurant cuisines_available Average cost
Food Heart Japnese, chinese 60$
Spice n Hungary Indian, American, mexican 42$
kfc, Lukestreet Thai, Japnese 29$
Brown bread shop American 11$
kfc, Hypert mall Thai, Japnese 40$
I want to create column which contains the no. of cuisines available
I am trying code
df['no._of_cuisines_available']=df['cuisines_available'].str.len()
Then instead of showing the no. of cuisines, it is showing the sum of charecters.
For example - for first row the o/p should be 2 , but its showing 17.
I need a new column that contain number of stores for each restaurant. example -
here kfc has 2 stores kfc, lukestreet and kfc, hypert mall. I have completely
no idea how to code this.
i)
df['cuisines_available'].str.split(',').apply(len)
ii)
df['Name_Restaurant'].str.split(',', expand=True).melt().['value'].str.strip().value_counts()
What ii) does: split columns at ',' and store all strings thus generated in an individual column. Then use melt to make one big column, strip away spaces etc. and count individual entries.
I'm trying to get the text inside a table in wikipedia, but I will do it for many cases (books in this case). I want to get the book genres.
Html code for the page
I need to extract the td containing the genre, when the text in Genre.
I did this:
page2 = urllib.request.urlopen(url2)
soup2 = BeautifulSoup(page2, 'html.parser')
for table in soup2.find_all('table', class_='infobox vcard'):
for tr in table.findAll('tr')[5:6]:
for td in tr.findAll('td'):
print(td.getText(separator="\n"))```
This gets me the genre but only in some pages due to the row count which differs.
Example of page where this does not work
https://en.wikipedia.org/wiki/The_Catcher_in_the_Rye (table on the right side)
Anyone knows how to search through string with "genre"? Thank you
In this particular case, you don't need to bother with all that. Just try:
import pandas as pd
tables = pd.read_html('https://en.wikipedia.org/wiki/The_Catcher_in_the_Rye')
print(tables[0])
Output:
0 1
0 First edition cover First edition cover
1 Author J. D. Salinger
2 Cover artist E. Michael Mitchell[1][2]
3 Country United States
4 Language English
5 Genre Realistic fictionComing-of-age fiction
6 Published July 16, 1951
7 Publisher Little, Brown and Company
8 Media type Print
9 Pages 234 (may vary)
10 OCLC 287628
11 Dewey Decimal 813.54
From here you can use standard pandas methods to extract whatever you need.
I have data in pandas dataframe. i need to extract all the content between the string which starts with "Impact Factor:" and ends with "&#". If the content doesn't have "Impact Factor:" i want null in that row of the dataframe
this is sample data from a single row.
Save to EndNote online &# Add to Marked List &# Impact Factor: Journal 2 and Citation Reports 500 &# Other Information &# IDS Number: EW5UR &#
I want the content like the below in a dataframe .
Journal 2 and Citation Reports 500
Journal 6 and Citation Reports 120
Journal 50 and Citation Reports 360
Journal 30 and Citation Reports 120
Hi you can just use a regular expression here:
result = your_df.your_col.apply(lambda x: re.findall('Impact Factor:(.*?)&#',x))
You may want to strip white spaces too in which case you could use:
result = your_df.your_col.apply(lambda x: re.findall('Impact Factor:\s*(.*?)\s*&#',x))
I have 48 matrices of dimensions 1,000 rows and 300,000 columns where each column has a respective ID, and each row is a measurement at one time point. Each of the 48 matrices is of the same dimension and their column IDs are all the same.
The way I have the matrices stored now is as RData objects and also as text files. I guess for SQL I'd have to transpose and store by ID, and in such case now the matrix would be of dimensions 300,000 rows and 1,000 columns.
I guess if I transpose it a small version of the data would look like this:
id1 1.5 3.4 10 8.6 .... 10 (with 1,000 columns, and 30,0000 rows now)
I want to store them in a way such that I can use R to retrieve a few of the rows (~ 5 to 100 each time).
The general strategy I have in mind is as follows:
(1) Create a database in sqlite3 using R that I will use to store the matrices (in different tables)
For file 1 to 48 (each file is of dim 1,000 rows and 300,000 columns):
(2) Read in file into R
(3) Store the file as a matrix in R
(4) Transpose the matrix (now its of dimensions 300,000 rows and 1,000 columns). Each row now is the unique id in the table in sqlite.
(5) Dump/write the matrix into the sqlite3 database created in (1) (dump it into a new table probably?)
Steps 1-5 are to create the DB.
Next, I need step 6 to read-in the database:
(6) Read some rows (at most 100 or so at a time) into R as a (sub)matrix.
A simple example code doing steps 1-6 would be best.
Some Thoughts:
I have used SQL before but it was mostly to store tabular data where each column had a name, in this case each column is just one point of the data matrix, I guess I could just name it col1 ... to col1000? or there are better tricks?
If I look at: http://sandymuspratt.blogspot.com/2012/11/r-and-sqlite-part-1.html they show this example:
dbSendQuery(conn = db,
"CREATE TABLE School
(SchID INTEGER,
Location TEXT,
Authority TEXT,
SchSize TEXT)")
But in my case this would look like:
dbSendQuery(conn = db,
"CREATE TABLE mymatrixdata
(myid TEXT,
col1 float,
col2 float,
.... etc.....
col1000 float)")
I.e., I have to type in col1 to ... col1000 manually, that doesn't sound very smart. This is where I am mostly stuck. Some code snippet would help me.
Then, I need to dump the text files into the SQLite database? Again, unsure how to do this from R.
Seems I could do something like this:
setwd(<directory where to save the database>)
db <- dbConnect(SQLite(), dbname="myDBname")
mymatrix.df = read.table(<full name to my text file containing one of the matrices>)
mymatrix = as.matrix(mymatrix.df)
Here I need to now the coe on how to dump this into the database...
Finally,
How to fast retrieve the values (without having to read the entire matrices each time) for some of the rows (by ID) using R?
From the tutorial it'd look like this:
sqldf("SELECT id1,id2,id30 FROM mymatrixdata", dbname = "Test2.sqlite")
But it the id1,id2,id30 are hardcoded in the code and I need to dynamically obtain them. I.e., sometimes i may want id1, id2, id10, id100; and another time i may want id80, id90, id250000, etc.
Something like this would be more approp for my needs:
cols.i.want = c("id1","id2","id30")
sqldf("SELECT cols.i.want FROM mymatrixdata", dbname = "Test2.sqlite")
Again, unsure how to proceed here. Code snippets would also help.
A simple example would help me a lot here, no need to code the whole 48 files, etc. just a simple example would be great!
Note: I am using Linux server, SQlite 3 and R 2.13 (I could update it as well).
In the comments the poster explained that it is only necessary to retrieve specific rows, not columns:
library(RSQLite)
m <- matrix(1:24, 6, dimnames = list(LETTERS[1:6], NULL)) # test matrix
con <- dbConnect(SQLite()) # could add dbname= arg. Here use in-memory so not needed.
dbWriteTable(con, "m", as.data.frame(m)) # write
dbGetQuery(con, "create unique index mi on m(row_names)")
# retrieve submatrix back as m2
m2.df <- dbGetQuery(con, "select * from m where row_names in ('A', 'C')
order by row_names")
m2 <- as.matrix(m2.df[-1])
rownames(m2) <- m2.df$row_names
Note that relational databases are set based and the order that the rows are stored in is not guaranteed. We have used order by row_names to get out a specific order. If that is not good enough then add a column giving the row index: 1, 2, 3, ... .
REVISED based on comments.