I have a really large csv file about 10GB. When ever I try to read in into iPython notebook using
data = pd.read_csv("data.csv")
my laptop gets stuck. Is it possible to just read like 10,000 rows or 500 MB of a csv file.
It is possible. You can create an iterator yielding chunks of your csv of a certain size at a time as a DataFrame by passing iterator=True with your desired chunksize to read_csv.
df_iter = pd.read_csv('data.csv', chunksize=10000, iterator=True)
for iter_num, chunk in enumerate(df_iter, 1):
print(f'Processing iteration {iter_num}')
# do things with chunk
Or more briefly
for chunk in pd.read_csv('data.csv', chunksize=10000):
# do things with chunk
Alternatively if there was just a specific part of the csv you wanted to read, you could use the skiprows and nrows options to start at a particular line and subsequently read n rows, as the naming suggests.
Likely a memory issue. On read_csv you can set chunksize (where you can specify number of rows).
Alternatively, if you don't need all the columns, you can change usecols on read_csv to import only the columns you need.
Related
I have a 3GB dataset with 40k rows and 60k columns which Pandas is unable to read and I would like to melt the file based on the current index.
The current file looks like this:
The first column is an index and I would like to melt all the file based on this index.
I tried pandas and dask, but all of them crush when reading the big file.
Do you have any suggestions?
thanks
You need to use the chunksize property of pandas. See for example How to read a 6 GB csv file with pandas.
You will process N rows at one time, without loading the whole dataframe. N will depend on your computer: if N is low, it will cost less memory but it will increase the run time and will cost more IO load.
# create an object reading your file 100 rows at a time
reader = pd.read_csv( 'bigfile.tsv', sep='\t', header=None, chunksize=100 )
# process each chunk at a time
for chunk in file:
result = chunk.melt()
# export the results into a new file
result.to_csv( 'bigfile_melted.tsv', header=None, sep='\t', mode='a' )
Furthermore, you can use the argument dtype=np.int32 for read_csv if you have integer or dtype=np.float32 to process data faster if you do not need precision.
NB: here you have examples of memory usage: Using Chunksize in Pandas.
I have File1 with "id,name" and File2 with "id,address". I cannot load the first file (less than 2Gb): it crashes after 76k rows (with chunk concat) and only 2 columns... I cannot read_csv on the second file too because it crashes the kernel after some rows loading.
I need to join the File1 and File2 with "id" but if I cannot put files in a dataframe variable I don't know how to do...
The file is only 5Gb with 30M rows but it crashes the kernel after few seconds of loading.
How to join the file without dataframing em please ?
I have tried with chucking but it crashes.
chunks = []
cols = [...]
for chunk in pd.read_csv("file2.csv", chunksize=500000, sep=',', error_bad_lines=False, low_memory=False, usecols=cols):
chunks.append(chunk)
df = pd.concat(chunks, axis=0)
print(f.shape)
I need the dataframe to load to join them or join the file without loading if possible
You read df2 chunk by chunk but since you append all the chunks, your resulting chunk is of the same size as your file2.
What you could do, if you are able to fully load your df1, is to join your df2 chunk by chunk like so :
for chunk in pd.read_csv("file2.csv", chunksize=500000, sep=',', error_bad_lines=False, low_memory=False, usecols=cols):
df1.merge(chunk, on =['id'], how='left')
Chunking like that will definitely still crash your kernel, since you're still trying to fit everything into memory. You need to do something to your chunks to reduce their size.
For instance, you could read both files in chunks, join each chunk, output the matches to another file, and keep the un-matched IDs in memory. That might still crash your kernel if you get unlucky though. It depends on what your performance constraints are, and what you need to do with your data afterwards.
I have a CSV file that doesn't fit into my system's memory. Using Pandas, I want to read a small number of rows scattered all over the file.
I think that I can accomplish this without pandas following the steps here: How to read specific lines of a large csv file
In pandas, I am trying to use skiprows to select only the rows that I need.
# FILESIZE is the number of lines in the CSV file (~600M)
# rows2keep is an np.array with the line numbers that I want to read (~20)
rows2skip = (row for row in range(0,FILESIZE) if row not in rows2keep)
signal = pd.read_csv('train.csv', skiprows=rows2skip)
I would expect this code to return a small dataframe pretty fast. However, what is does is start consuming memory over several minutes until the system becomes irresponsive. I'm guessing that it is reading the whole dataframe first and will get rid of rows2skip later.
Why is this implementation so inefficient? How can I efficiently create a dataframe with only the lines specified in rows2keep?
Try this
train = pd.read_csv('file.csv', iterator=True, chunksize=150000)
If you only want to read the first n rows:
train = pd.read_csv(..., nrows=n)
If you only want to read rows from n to n+100
train = pd.read_csv(..., skiprows=n, nrows=n+100)
chunksize should help in limiting the memory usage. Alternatively, if you only need a few number of lines, a possible way is to first read the required lines ouside of pandas and then only feed read_csv with that subset. Code could be:
lines = [line for i, line in enumerate(open('train.csv')) if i in lines_to_keep]
signal = pd.read_csv(io.StringIO(''.join(lines)))
I have a large .xlsx file with 1 million rows. I don't want to open the whole file in one go. I was wondering if I can read a chunk of the file, process it and then read the next chunk? (I prefer to use pandas for it.)
UPDATE: 2019-09-05
The chunksize parameter has been deprecated as it wasn't used by pd.read_excel(), because of the nature of XLSX file format, which will be read up into memory as a whole during parsing.
There are more details about that in this great SO answer...
OLD answer:
you can use read_excel() method:
chunksize = 10**5
for chunk in pd.read_excel(filename, chunksize=chunksize):
# process `chunk` DF
if your excel file has multiple sheets, take a look at bpachev's solution
Yes. Pandas supports chunked reading. You would go about reading an excel file like so.
import pandas as pd
xl = pd.ExcelFile("myfile.xlsx")
for sheet_name in xl.sheet_names:
reader = xl.parse(sheet_name, chunksize=1000):
for chunk in reader:
#parse chunk here
I am exploring switching to python and pandas as a long-time SAS user.
However, when running some tests today, I was surprised that python ran out of memory when trying to pandas.read_csv() a 128mb csv file. It had about 200,000 rows and 200 columns of mostly numeric data.
With SAS, I can import a csv file into a SAS dataset and it can be as large as my hard drive.
Is there something analogous in pandas?
I regularly work with large files and do not have access to a distributed computing network.
Wes is of course right! I'm just chiming in to provide a little more complete example code. I had the same issue with a 129 Mb file, which was solved by:
import pandas as pd
tp = pd.read_csv('large_dataset.csv', iterator=True, chunksize=1000) # gives TextFileReader, which is iterable with chunks of 1000 rows.
df = pd.concat(tp, ignore_index=True) # df is DataFrame. If errors, do `list(tp)` instead of `tp`
In principle it shouldn't run out of memory, but there are currently memory problems with read_csv on large files caused by some complex Python internal issues (this is vague but it's been known for a long time: http://github.com/pydata/pandas/issues/407).
At the moment there isn't a perfect solution (here's a tedious one: you could transcribe the file row-by-row into a pre-allocated NumPy array or memory-mapped file--np.mmap), but it's one I'll be working on in the near future. Another solution is to read the file in smaller pieces (use iterator=True, chunksize=1000) then concatenate then with pd.concat. The problem comes in when you pull the entire text file into memory in one big slurp.
This is an older thread, but I just wanted to dump my workaround solution here. I initially tried the chunksize parameter (even with quite small values like 10000), but it didn't help much; had still technical issues with the memory size (my CSV was ~ 7.5 Gb).
Right now, I just read chunks of the CSV files in a for-loop approach and add them e.g., to an SQLite database step by step:
import pandas as pd
import sqlite3
from pandas.io import sql
import subprocess
# In and output file paths
in_csv = '../data/my_large.csv'
out_sqlite = '../data/my.sqlite'
table_name = 'my_table' # name for the SQLite database table
chunksize = 100000 # number of lines to process at each iteration
# columns that should be read from the CSV file
columns = ['molecule_id','charge','db','drugsnow','hba','hbd','loc','nrb','smiles']
# Get number of lines in the CSV file
nlines = subprocess.check_output('wc -l %s' % in_csv, shell=True)
nlines = int(nlines.split()[0])
# connect to database
cnx = sqlite3.connect(out_sqlite)
# Iteratively read CSV and dump lines into the SQLite table
for i in range(0, nlines, chunksize):
df = pd.read_csv(in_csv,
header=None, # no header, define column header manually later
nrows=chunksize, # number of rows to read at each iteration
skiprows=i) # skip rows that were already read
# columns to read
df.columns = columns
sql.to_sql(df,
name=table_name,
con=cnx,
index=False, # don't use CSV file index
index_label='molecule_id', # use a unique column from DataFrame as index
if_exists='append')
cnx.close()
Below is my working flow.
import sqlalchemy as sa
import pandas as pd
import psycopg2
count = 0
con = sa.create_engine('postgresql://postgres:pwd#localhost:00001/r')
#con = sa.create_engine('sqlite:///XXXXX.db') SQLite
chunks = pd.read_csv('..file', chunksize=10000, encoding="ISO-8859-1",
sep=',', error_bad_lines=False, index_col=False, dtype='unicode')
Base on your file size, you'd better optimized the chunksize.
for chunk in chunks:
chunk.to_sql(name='Table', if_exists='append', con=con)
count += 1
print(count)
After have all data in Database, You can query out those you need from database.
If you want to load huge csv files, dask might be a good option. It mimics the pandas api, so it feels quite similar to pandas
link to dask on github
You can use Pytable rather than pandas df.
It is designed for large data sets and the file format is in hdf5.
So the processing time is relatively fast.