I am trying to import a .csv file from my Downloads folder.
Usually, the read_csv function will import the entire rows, though there are millions of rows.
In this case, my file has 236,905 rows, but exactly 100,000 are loaded.
df = pd.read_csv(r'C:\Users\user\Downloads\df.csv',nrows=9999999,low_memory=False)
I come across the same problem with a file containing 5M rows.
I tried first this option :
tp = pd.read_csv('yourfile.csv', iterator=True, chunksize=1000)
data_customers = pd.concat(tp, ignore_index=True)
It did work but in my case some rows where not read properly since some columns contained the character ',' which is used as delimiter in read_csv
The other solution is to use Dask It has an object called "DataFrame" (as Pandas). Dask reads your file and construct a dask dataframe composed of several pandas dataframe.
It's a great solution for parallel computing.
Hope it helps
You need to create chunks using the chunksize= parameter:
temporary = pd.read_csv(r'C:\Users\user\Downloads\df.csv', iterator=True, chunksize=1000)
df = pd.concat(temporary, ignore_index=True)
ignore_index resets the index so it's not repeating.
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 try to use multiprocessing to read the csv file faster than using read_csv.
df = pd.read_csv('review-1m.csv', chunksize=10000)
But the df I get is not the dataframe but of the type pandas.io.parsers.TextFileReader. So I try to use
df = pd.concat(tp, ignore_index=True)
to convert df into a dataframe. But this process takes a lot of time thus the result is not much different from directly using read_csv. Does anyone know that how to make the process of converting df into dataframe faster?
pd.read_csv() is likely going to give you the same read time as any other method. If you want a real performance increase you should change the format you store your file in.
http://pandas.pydata.org/pandas-docs/stable/io.html#performance-considerations
I have 30 csv files. I want to give it as input in for loop, in pandas?
Each file has names such as fileaa, fileab,fileac,filead,....
I have multiple input files and And i would like to receive one output.
Usually i use read_csv but due to memory error, 'read_csv' doesn't work.
f = "./file.csv"
df = pd.read_csv(f, sep="/", header=0, dtype=str)
So i would like to try parallel processing in python 2.7
You might want to have a look at dask.
Dask docs show a demo on how to read in many csv files and output a single dask dataframe:
import dask.dataframe as dd
df = dd.read_csv('*.csv')
And then MANY (but not all) of the pandas methods are available, i.e.:
df.head()
It would be useful to read more on dask dataframe to understand difference with pandas dataframe
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.