I am trying to load csv files in pandas dataframe. However, Python is taking very large amount of memory while loading the files. For example, the size of csv file is 289 MB but the memory usage goes to around 1700 MB while I am trying to load the file. And at that point, the system shows memory error. I have also tried chunk size but the problem persists. Can anyone please show me a way forward?
OK, first things first, do not confuse disk size and memory size. A csv, in it's core is a plain text file, whereas a pandas dataframe is a complex object loaded in memory. That said, I can't give a statement about your particular case, considering that I don't know what you have in your csv. So instead I'll give you an example with a csv on my computer that has a similar size:
-rw-rw-r-- 1 alex users 341M Jan 12 2017 cpromo_2017_01_12_rec.csv
Now reading the CSV:
>>> import pandas as pd
>>> df = pd.read_csv('cpromo_2017_01_12_rec.csv')
>>> sys:1: DtypeWarning: Columns (9) have mixed types. Specify dtype option on import or set low_memory=False.
>>> df.memory_usage(deep=True).sum() / 1024**2
1474.4243307113647
Pandas will attempt to optimize it as much as it can, but it won't be able to do the impossible. If you are low on memory, this answer is a good place to start. Alternatively you could try dask but I think that's too much work for a small csv.
You can use the library "dask" e.g:
# Dataframes implement the Pandas API
import dask.dataframe as dd`<br>
df = dd.read_csv('s3://.../2018-*-*.csv')
try like this - 1) load with dask and then 2) convert to pandas
import pandas as pd
import dask.dataframe as dd
import time
t=time.clock()
df_train = dd.read_csv('../data/train.csv', usecols=[col1, col2])
df_train=df_train.compute()
print("load train: " , time.clock()-t)
Related
I would like to benefit dask repartition feature, but the requested size is not fulfilled, and smaller files are produced.
import pandas as pd
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import dask.dataframe as dd
file = 'example.parquet'
file_res_dd = 'example_res'
# Generate a random df and write it down as an input data file.
df = pd.DataFrame(np.random.randint(100,size=(100000, 20)),columns=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T'])
table = pa.Table.from_pandas(df)
pq.write_table(table, file, version='2.0')
# Read back with dask, repartition, and write it down.
dd_df = dd.read_parquet(file, engine='pyarrow')
dd_df = dd_df.repartition(partition_size='1MB')
dd_df.to_parquet(file_res_dd, engine='pyarrow')
With this example, I am expecting files with size about 1MB.
Input file that is written 1st is about 1,7MB, so I am expecting 2 files at most.
But in the example_res folder that is created, I get 9 files being ~270kB.
Why is that so?
Thanks for your help! Bests,
The "partition size" is of the in-memory representation, and only an approximation.
Parquet offers various encoding and compression options that generally result in a file that is a good deal smaller - but how much smaller will depend greatly on the specific data in question.
I have a csv that I am reading into a Pandas DataFrame but it takes about 35 minutes to read. The csv is approximately 120 GB. I found a module called cudf that allows a GPU DataFrame however it is only for Linux. Is there something similar for Windows?
chunk_list = []
combined_array = pd.DataFrame()
for chunk in tqdm(pd.read_csv('\\large_array.csv', header = None,
low_memory = False, error_bad_lines = False, chunksize = 10000)):
print(' --- Complete')
chunk_list.append(chunk)
array = pd.concat(chunk_list)
print(array)
You can also look at dask-dataframe if you really want to read it into a pandas api like dataframe.
For reading csvs , this will parallelize your io task across multiple cores plus nodes. This will probably alleviate memory pressures by scaling across nodes as with 120 GB csv you will probably be memory bound too.
Another good alternative might be using arrow.
Do you have GPU ? if yes, please look at BlazingSQL, the GPU SQL engine in a Python package.
In this article, describe Querying a Terabyte with BlazingSQL. And BlazingSQL support read from CSV.
After you get GPU dataframe convert to Pandas dataframe with
# from cuDF DataFrame to pandas DataFrame
df = gdf.to_pandas()
Just was wondering if there is a way to improve the performance of reading large csv files into a pandas dataframe. I have 3 large (3.5MM records each) pipe delimited file which I want to load into dataframe and perform some task on it. Currently I am using pandas.read_csv() defining the cols and there datatypes in the parameter like below. I did see some improvement by defining the datatype of the columns but it still takes more than 3 minutes to load.
import pandas as pd
df = pd.read_csv(file_, index_col=None, usecols = sourceFields, sep='|', header=0, dtype={'date':'str', 'gwTimeUtc':'str', 'asset':'|str',
'instrumentId':'|str', 'askPrice':'float64', 'bidPrice':'float64',
'askQuantity':'float64', 'bidQuantity':'float64', 'currency':'|str',
'venue':'|str', 'owner':'|str', 'status':'|str', 'priceNotation':'|str', 'nominalQuantity':'float64'})
Depending on what you wish to do with the data, a good option is dask.dataframe. This library works out-of-memory, and allows you to perform a subset of pandas operations lazily. You can then bring the results in memory as a pandas dataframe. Below is example code you can try:
import dask.dataframe as dd, pandas as pd
# point to all files beginning with "file"
dask_df = dd.read_csv('file*.csv')
# define your calculations as you would in pandas
dask_df['col2'] = dask_df['col1'] * 2
# compute results & return to pandas
df = dask_df.compute()
Crucially, nothing significant is computed until the very last line.
The .feather file is significantly faster than .csv. Pandas has built-in support for feather files.
Read the csv in using pd.read_csv(path) and then export it to a feather file: pd.to_feather(path). Now, read the feather file instead of csv.
In my case, a 950 MB csv file was compressed to a 180 MB feather file. Instead of taking 30 seconds to read, it takes about 1 second. I know I am a bit late to the party, but feather files are seriously underrated.
I work for a company and I recently switched from using spreadsheet package to python. Since, I am very new to python there are alot of things that I have difficulty grasping.Using python, I am trying to extract data from a large csv file(37791 rows and 316 columns.) Here is a piece of code I wrote:
Solution 1
import numpy as np
import pandas as pd
df=pd.read_csv=('C:\\Users\\Maxwell\\Desktop\\Test.data.csv',skiprows=1)
data=df.loc[:,['Steps','Parameter']]
This command generates an error,i.e, it gives a DtypeWwarning:columns (0,1,2,3........81) have mixed types. Specify dtype option on import or set low memory= False
So, I found a workaround.
Solution 2
import pandas as pd
import numpy as np
df=pd.read_csv(('C:\\Users\\Maxwell\\Desktop\\Test.data.csv',skiprows=1,error_bad_lines=False, index_col=False, dtype='unicode')
data=df.loc[:,['Steps','Parameter']]
Two questions:
i)I was able to get around the error, but now the columns that I want(Steps & Parameter)have been converted to objects(probably due to the dtype='unicode' command). How can I convert Steps column into an integer type and parameter into a float.
ii) Some people say that dtype warning isn't really an error. But, I found out that when I use Solution 1 and read the csv file. The Steps column contains some floats.The original csv file doesn't have any floats in Steps column. It looks as if, some floats have been placed by python itself!! Why does this happen?
(I am not able to upload the original csv file, because my company doesn't allow it!)
I am attempting to export a text file into a csv. The file is very large (1.6 million rows) tab delimited file. When I export the file using to_csv it only exports 1048576 rows. Is there a maximum amount of rows that to_csv will export?
should I export the data in a different way? I would really like to be able to get it into a csv.
here is an example of my code.
import pandas as pd
import numpy as np
import os
from pandas import Series, DataFrame
pathDataEDM = "C:/Users/FILE.txt"
dataEDM = pd.read_csv(pathDataEDM, sep="\t")
dataEDM.to_csv(os.path.join(ExportDir),index=False)
Pandas doesn't have a limit. However, most tools you use to open csv files like LibreOffice calc or excel can only display a maximum of 1048576 rows.
To prove the point, try print(df) and all the 1.6 million rows would be displayed by pandas
I don't think there is a maximum (since it is not documented and 1.6 million is quite low for the maximum).
You can try specifying the following optional arguments (see the docs):
chunksize : int or None
rows to write at a time
compression : string, optional
a string representing the compression to use in the output file,
allowed values are ‘gzip’, ‘bz2’, ‘xz’, only used when the first
argument is a filename