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Is there a way with Python + Pandas to save small chunk files individually?

I am very new to programming and I'm currently trying to breakdown a CSV file into bite-sized chunks and saving those chunks as individual files. I was able to do it using a super roundabout way but now I want to figure out how to write a function that does this for me.
This is the code I have now:
import pandas as pd
def chunky(file, chunksize, iterator, rounds):
df = pd.read_csv(file, chunksize = chunksize, iterator = iterator)
count = 0
while count < rounds:
count += 1
file = next(df)
file.to_csv("output.csv")
However, when I do this it will print the output as one file but I am looking to save multiple individual files. Any and all help is appreciated! Thank you so much in advance :)

Don't use both chunksize and iterator.
Also make sure you create a new output file for each subfile.
The code below will create a set of new files up to 2000 rows long. The new output files are named after the input file, but with a number with leading zeros (change the 5 accordingly if you need more or less files).
import os
reader = pd.read_csv(filename, chunksize=2000)
for i, df in enumerate(reader):
output = os.path.splitext(filename)[0] + f'-{i+1:05d}.csv'
df.to_csv(output, index=False)

Data from multiple sensors saved to txt file imported to pandas

Good day everyone.
I was hoping someone here could help me with a bit of a problem. I've run an experiment, where data has been gathered from 6 separate sensors simultaneously. The data has then been exported to a single shared txt file. Now I need to import the data to python to analyze it.
I know I can do this by taking each of the lines and simply copy&pasting data output from each sensor into a separate document, and then import those in a loop - but that is a lot of work and brings in a high potential of human error.
But is there no way of using readline with specific lines read, and porting that to pandas DataFrame? There is a fixed header spacing, and line spacing between each sensor.
I tried:
f=open('OR0024622_auto3200.txt')
lines = f.readlines()
base = 83
sensorlines = 6400
Sensor=[]
Sensor = lines[base:sensorlines+base]
df_sens = pd.DataFrame(Sensor)
df_sens
but the output isn't very useful:
Snip from of Output
--
Here's the file i am importing:
link.
Any suggestions ?

Looks like a tab separated data.
use
>>> df = pd.read_csv('OR0024622_auto3200.txt', delimiter=r'\t', skiprows=83, header=None, nrows=38955-84)
>>> df.tail()
0 1 2
38686 6397 3.1980000000e+003 9.28819e-009
38687 6398 3.1985000000e+003 9.41507e-009
38688 6399 3.1990000000e+003 1.11703e-008
38689 6400 3.1995000000e+003 9.64276e-009
38690 6401 3.2000000000e+003 8.92203e-009
>>> df.head()
0 1 2
0 1 0.0000000000e+000 6.62579e+000
1 2 5.0000000000e-001 3.31289e+000
2 3 1.0000000000e+000 2.62362e-011
3 4 1.5000000000e+000 1.51130e-011
4 5 2.0000000000e+000 8.35723e-012

abhilb's answer is to the point and correct, but there is a lot to be said regarding loading/reading files. A quick browser search will take you a long way (I encourage you to read up on this!), but I'll add a few details here:
If you want to load multiple files that match a pattern you can do so iteratively via glob:
import pandas as pd
from glob import glob as gg
filePattern = "/path/to/file/*.txt"
for fileName in gg(filePattern):
df = pd.read_csv('OR0024622_auto3200.txt', delimiter=r'\t')
This will load each file one-by-one. What if you want to put all data into a single dataframe? Do this:
masterDF = pd.Dataframe()
for fileName in gg(filePattern):
df = pd.read_csv('OR0024622_auto3200.txt', delimiter=r'\t')
masterDF = pd.concat([masterDF, df], axis=0)
This works great for pandas, but what if you want to read into a numpy array?
import numpy as np
# using previous imports
base = 83
sensorlines = 6400
# create an empty array that has three columns
masterArray = np.full((0, 3), np.nan)
for fileName in gg(filePattern):
# open the file (NOTE: this does not read the file, just puts it in a buffer)
with open(fileName, "r") as tmp:
# now read the file and split each line by the carriage return (could be "\r\n")
# you now have a list of strings
data = tmp.read().split("\n")
# keep only the "data" portion of the file
data = data[base:sensorlines + base]
# convert list of strings to an array of floats
# here, I use a "list comprehension" for speed and simplicity
data = np.array([r.split("\t") for r in data]).astype(float)
# stack your new data onto your master array
masterArray = np.vstack([masterArray, data])
Opening a file via the "with open(fileName, "r")" syntax is handy because Python automatically closes the file when you are done. If you don't use "with" then you must manually close the file (e.g. tmp.close()).
These are just some starting points to get you on your way. Feel free to ask for clarification.

Read only specific fields from large JSON and import into a Pandas Dataframe

I have a folder with more or less 10 json files that size between 500 and 1000 Mb.
Each file contains about 1.000.000 of lines like the loffowling:
{
"dateTime": '2019-01-10 01:01:000.0000'
"cat": 2
"description": 'This description'
"mail": 'mail#mail.com'
"decision":[{"first":"01", "second":"02", "third":"03"},{"first":"04", "second":"05", "third":"06"}]
"Field001": 'data001'
"Field002": 'data002'
"Field003": 'data003'
...
"Field999": 'data999'
}
My target is to analyze it with pandas so I would like to save the data coming from all the files into a Dataframe.
If I loop all the files Python crash because I don't have free resources to manage the data.
As for my purpose I only need a Dataframe with two columns cat and dateTime from all the files, which I suppose is lighter that a whole Dataframe with all the columns I have tryed to read only these two columns with the following snippet:
Note: at the moment I am working with only one file, when I get a fast reader code I will loop to all other files (A.json, B.json, ...)
import pandas as pd
import json
import os.path
from glob import glob
cols = ['cat', 'dateTime']
df = pd.DataFrame(columns=cols)
file_name='this_is_my_path/File_A.json'
with open(file_name, encoding='latin-1') as f:
for line in f:
data=json.loads(line)
lst_dict=({'cat':data['cat'], 'dateTime':data['dateTime']})
df = df.append(lst_dict, ignore_index=True)
The code works, but it is very very slow so it takes more than one hour for one, file while reading all the file and storing into a Dataframe usually takes me 8-10 minutes.
Is there a way to read only two specific columns and append to a Dataframe in a faster way?
I have tryed to read all the JSON file and store into a Dataframe, then drop all the columns but 'cat' and 'dateTime' but it seems to be too heavy for my MacBook.

I had the same problem. I found out that appending a dict to a DataFrame is very very slow. Extract the values as a list instead. In my case it took 14 s instead of 2 h.
cols = ['cat', 'dateTime']
data = []
file_name = 'this_is_my_path/File_A.json'
with open(file_name, encoding='latin-1') as f:
for line in f:
doc = json.loads(line)
lst = [doc['cat'], doc['dateTime']]
data.append(lst)
df = pd.DataFrame(data=data, columns=cols)

Will this help?
Step 1.
Read your json file from pandas
"pandas.read_json() "
Step 2.
Then filter out your 2 columns from the dataframe.
Let me know if you still face any issue.
Thanks

Sequentially read huge CSV file in python

I have a 10gb CSV file that contains some information that I need to use.
As I have limited memory on my PC, I can not read all the file in memory in one single batch. Instead, I would like to iteratively read only some rows of this file.
Say that at the first iteration I want to read the first 100, at the second those going to 101 to 200 and so on.
Is there an efficient way to perform this task in Python?
May Pandas provide something useful to this? Or are there better (in terms of memory and speed) methods?

Here is the short answer.
chunksize = 10 ** 6
for chunk in pd.read_csv(filename, chunksize=chunksize):
process(chunk)
Here is the very long answer.
To get started, you’ll need to import pandas and sqlalchemy. The commands below will do that.
import pandas as pd
from sqlalchemy import create_engine
Next, set up a variable that points to your csv file. This isn’t necessary but it does help in re-usability.
file = '/path/to/csv/file'
With these three lines of code, we are ready to start analyzing our data. Let’s take a look at the ‘head’ of the csv file to see what the contents might look like.
print pd.read_csv(file, nrows=5)
This command uses pandas’ “read_csv” command to read in only 5 rows (nrows=5) and then print those rows to the screen. This lets you understand the structure of the csv file and make sure the data is formatted in a way that makes sense for your work.
Before we can actually work with the data, we need to do something with it so we can begin to filter it to work with subsets of the data. This is usually what I would use pandas’ dataframe for but with large data files, we need to store the data somewhere else. In this case, we’ll set up a local sqllite database, read the csv file in chunks and then write those chunks to sqllite.
To do this, we’ll first need to create the sqllite database using the following command.
csv_database = create_engine('sqlite:///csv_database.db')
Next, we need to iterate through the CSV file in chunks and store the data into sqllite.
chunksize = 100000
i = 0
j = 1
for df in pd.read_csv(file, chunksize=chunksize, iterator=True):
df = df.rename(columns={c: c.replace(' ', '') for c in df.columns})
df.index += j
i+=1
df.to_sql('table', csv_database, if_exists='append')
j = df.index[-1] + 1
With this code, we are setting the chunksize at 100,000 to keep the size of the chunks managable, initializing a couple of iterators (i=0, j=0) and then running a through a for loop. The for loop read a chunk of data from the CSV file, removes space from any of column names, then stores the chunk into the sqllite database (df.to_sql(…)).
This might take a while if your CSV file is sufficiently large, but the time spent waiting is worth it because you can now use pandas ‘sql’ tools to pull data from the database without worrying about memory constraints.
To access the data now, you can run commands like the following:
df = pd.read_sql_query('SELECT * FROM table', csv_database)
Of course, using ‘select *…’ will load all data into memory, which is the problem we are trying to get away from so you should throw from filters into your select statements to filter the data. For example:
df = pd.read_sql_query('SELECT COl1, COL2 FROM table where COL1 = SOMEVALUE', csv_database)

You can use pandas.read_csv() with chuncksize parameter:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html#pandas.read_csv
for chunck_df in pd.read_csv('yourfile.csv', chunksize=100):
# each chunck_df contains a part of the whole CSV

This code may help you for this task. It navigates trough a large .csv file and does not consume lots of memory so that you can perform this in a standard lap top.
import pandas as pd
import os
The chunksize here orders the number of rows within the csv file you want to read later
chunksize2 = 2000
path = './'
data2 = pd.read_csv('ukb35190.csv',
chunksize=chunksize2,
encoding = "ISO-8859-1")
df2 = data2.get_chunk(chunksize2)
headers = list(df2.keys())
del data2
start_chunk = 0
data2 = pd.read_csv('ukb35190.csv',
chunksize=chunksize2,
encoding = "ISO-8859-1",
skiprows=chunksize2*start_chunk)
headers = []
for i, df2 in enumerate(data2):
try:
print('reading cvs....')
print(df2)
print('header: ', list(df2.keys()))
print('our header: ', headers)
# Access chunks within data
# for chunk in data:
# You can now export all outcomes in new csv files
file_name = 'export_csv_' + str(start_chunk+i) + '.csv'
save_path = os.path.abspath(
os.path.join(
path, file_name
)
)
print('saving ...')
except Exception:
print('reach the end')
break

Method to transfer huge CSV into database is good because we can easily use SQL query.
We have also to take into account two things.
FIRST POINT: SQL also are not a rubber, it will not be able to stretch the memory.
For example converted to bd file:
https://nycopendata.socrata.com/Social-Services/311-Service-Requests-
from-2010-to-Present/erm2-nwe9
For this db file SQL language:
pd.read_sql_query("SELECT * FROM 'table'LIMIT 600000", Mydatabase)
It can read maximum about 0,6 mln records no more with 16 GB RAM memory of PC (time of operation 15,8 second).
It could be malicious to add that downloading directly from a csv file is a bit more efficient:
giga_plik = 'c:/1/311_Service_Requests_from_2010_to_Present.csv'
Abdul = pd.read_csv(giga_plik, nrows=1100000)
(time of operation 16,5 second)
SECOND POINT: To effectively using SQL data series converted from CSV we ought to memory about suitable form of date. So I proposer add to ryguy72's code this:
df['ColumnWithQuasiDate'] = pd.to_datetime(df['Date'])
All code for file 311 as about I pointed:
start_time = time.time()
### sqlalchemy create_engine
plikcsv = 'c:/1/311_Service_Requests_from_2010_to_Present.csv'
WM_csv_datab7 = create_engine('sqlite:///C:/1/WM_csv_db77.db')
#----------------------------------------------------------------------
chunksize = 100000
i = 0
j = 1
## --------------------------------------------------------------------
for df in pd.read_csv(plikcsv, chunksize=chunksize, iterator=True, encoding='utf-8', low_memory=False):
df = df.rename(columns={c: c.replace(' ', '') for c in df.columns})
## -----------------------------------------------------------------------
df['CreatedDate'] = pd.to_datetime(df['CreatedDate']) # to datetimes
df['ClosedDate'] = pd.to_datetime(df['ClosedDate'])
## --------------------------------------------------------------------------
df.index += j
i+=1
df.to_sql('table', WM_csv_datab7, if_exists='append')
j = df.index[-1] + 1
print(time.time() - start_time)
At the end I would like to add: converting a csv file directly from the Internet to db seems to me a bad idea. I propose to download base and convert locally.

pandas HDF select does not recognise column name

I'm trying to process a large (2gb) csv file on a machine with only 4gb of RAM (don't ask) to produce a different, formatted csv containing a subset of data that needs some processing. I'm reading the file and creating a HDFstore that I query later for the data that I require for output. Everything works except that I cant retrieve data from the store using Term - error message comes back that PLOT is not a column name. Individual variables look fine and the store is what I expect I just can't see where the error is. (nb pandas v14 and numpy1.9.0). Very new to this so apologies for the clunky code.
#wibble wobble -*- coding: utf-8 -*-
# short version
def filesport():
import pandas as pd
import numpy as np
from pandas.io.pytables import Term
Location = r"CL_short.csv"
store = pd.HDFStore('blarg.h5')
maxlines = sum(1 for line in open (Location))
print maxlines
#set chunk small for test file
chunky=4
plotty =pd.DataFrame(columns=['PLOT'])
dfdum=pd.DataFrame(columns=['PLOT', 'mDate', 'D100'])
#read file in chunks to avoid RAM blowing up
bucket = pd.read_csv(Location, iterator=True, chunksize=chunky, usecols= ['PLOT','mDate','D100'])
for chunk in bucket:
store.append('wibble', chunk, format='table', data_columns=['PLOT','mDate','D100'], ignore_index=True)
#retrieve plot numbers and select unique items
plotty = store.select('wibble', "columns = ['PLOT']")
plotty.drop_duplicates(inplace=True)
#iterate through unique plots to retrieve data and put in dataframe for output
for index, row in plotty.iterrows():
dfdum = store.select('wibble', [Term('PLOT', '=', plotty.iloc[index]['PLOT'])])
#process dfdum for output to new csv
print("successful completion")
filesport()

Final listing for those that wish to fight through the tumbleweed to reach here and are similarly bemused by processing large .csv files and the various methods of trying to retrieve/process data. The biggest problem was getting the sytax of the pytables Term right. Despite several examples indicating that it was possible to use 'A >20' etc this never worked for me. I set up a string condition containing the Term query and this worked (it is in the documentation TBF).
Also found it easier to query the HDF to retrieve unique items direct from the store in a list which could then be sorted and iterated through to retrieve data plot by plot. Note that I wanted the final csv file to have plot and then all the D100 data in date order, hence the pivot at the end.
Reading the csv file in chunks meant that each plot retrieved from the store had a header and this got written to the final csv which messed things up. I'm sure there's a more elegant way of only writing one header than the one I've shown here.
It works, takes about 2 hours to process the data and produce the final csv file (initial file 2GB, 30+million lines, data for 100,000+ unique plots, machine has 4GB of RAM but running 32-bit which means that only 2.5GB of RAM was available).
Good luck if you have a similar problem, and I hope you find this useful
#wibble wobble -*- coding: utf-8 -*-
def filesport():
import pandas as pd
import numpy as np
from pandas.io.pytables import Term
print (pd.__version__)
print (np.__version__)
Location = r"conliq_med.csv"
store = pd.HDFStore('blarg.h5')
maxlines = sum(1 for line in open (Location))
print maxlines
chunky=100000
#read file in chunks to avoid RAM blowing up select only needed columns
bucket = pd.read_csv(Location, iterator=True, chunksize=chunky, usecols= ['PLOT','mDate','D100'])
for chunk in bucket:
store.append('wibble', chunk, format='table', data_columns=['PLOT','mDate','D100'], ignore_index=True)
#retrieve unique plots and sort
plotty = store.select_column('wibble', 'PLOT').unique()
plotty.sort()
#set flag for writing file header
i=0
#iterate through unique plots to retrieve data and put in dataframe for output
for item in plotty:
condition = 'PLOT =' + str(item)
dfdum = store.select('wibble', [Term(condition)])
dfdum["mDate"]= pd.to_datetime(dfdum["mDate"], dayfirst=True)
dfdum.sort(columns=["PLOT", "mDate"], inplace=True)
dfdum["mDate"] = dfdum["mDate"].map(lambda x: x.strftime("%Y - %m"))
dfdum=dfdum.pivot("PLOT", "mDate", "D100")
#only print one header to file
if i ==0:
dfdum.to_csv("CL_OP.csv", mode='a')
i=1
else:
dfdum.to_csv("CL_OP.csv", mode='a', header=False)
print("successful completion")
filesport()