I am trying to get the dimensions (shape) of a data frame using pandas in python without reading the entire data frame first in memory given that the file is quite large.
To get the number of columns with minimal loading of the file into the memory, I can for example use the argument below.
import pandas as pd
pd = pd.read_csv("myData.csv", nrows=1)
print(pd.shape)
To get the row numbers I can use the argument usecols = [1] when reading the file but there must be a simpler way of doing this.
If there are other packages or scripts that can easily give me such metadata information, I would be happy as well. It is really metadata I am looking for such as column names, number of rows, number of columns etc but I don't want to read the entire file in!
You don't even need pandas for this. Use the built-in csv module to parse the file:
import csv
with open('myData.csv')as fp:
reader = csv.reader(fp)
headers = next(reader) # The header row is now consumed
ncol = len(headers)
nrow = sum(1 for _ in reader) # What remains are the data rows
Related
Trying to whip this out in python. Long story short I got a csv file that contains column data i need to inject into another file that is pipe delimited. My understanding is that python can't replace values, so i have to re-write the whole file with the new values.
data file(csv):
value1,value2,iwantthisvalue3
source file(txt, | delimited)
value1|value2|iwanttoreplacethisvalue3|value4|value5|etc
fixed file(txt, | delimited)
samevalue1|samevalue2| replacedvalue3|value4|value5|etc
I can't figure out how to accomplish this. This is my latest attempt(broken code):
import re
import csv
result = []
row = []
with open("C:\data\generatedfixed.csv","r") as data_file:
for line in data_file:
fields = line.split(',')
result.append(fields[2])
with open("C:\data\data.txt","r") as source_file, with open("C:\data\data_fixed.txt", "w") as fixed_file:
for line in source_file:
fields = line.split('|')
n=0
for value in result:
fields[2] = result[n]
n=n+1
row.append(line)
for value in row
fixed_file.write(row)
I would highly suggest you use the pandas package here, it makes handling tabular data very easy and it would help you a lot in this case. Once you have installed pandas import it with:
import pandas as pd
To read the files simply use:
data_file = pd.read_csv("C:\data\generatedfixed.csv")
source_file = pd.read_csv('C:\data\data.txt', delimiter = "|")
and after that manipulating these two files is easy, I'm not exactly sure how many values or which ones you want to replace, but if the length of both "iwantthisvalue3" and "iwanttoreplacethisvalue3" is the same then this should do the trick:
source_file['iwanttoreplacethisvalue3'] = data_file['iwantthisvalue3]
now all you need to do is save the dataframe (the table that we just updated) into a file, since you want to save it to a .txt file with "|" as the delimiter this is the line to do that (however you can customize how to save it in a lot of ways):
source_file.to_csv("C:\data\data_fixed.txt", sep='|', index=False)
Let me know if everything works and this helped you. I would also encourage to read up (or watch some videos) on pandas if you're planning to work with tabular data, it is an awesome library with great documentation and functionality.
So I have a several tables in the format of csv, I am using Python and the csv module. I want to extract a particular value, lets say column=80 row=109.
Here is a random example:
import csv
with open('hugetable.csv', 'r') as file:
reader = csv.reader(file)
print(reader[109][80])
I am doing this many times with large tables and I would like to avoid loading the whole table into an array (line 2 above) to ask for a single value. Is there a way to open the file, load the specific value and close it again? Would this process be more efficient than what I have done above?
Thanks for all the answers, all answers so far work pretty well.
You could try reading the file without csv library:
row = 108
column = 80
with open('hugetable.csv', 'r') as file:
header = next(file)
for _ in range(row-1):
_ = next(file)
line = next(file)
print(line.strip().split(',')[column])
You can try pandas to load only certain columns of your csv file
import pandas as pd
pd.read_csv('foo.csv',usecols=["column1", "column2"])
You could use pandas to load it
import pandas as pd
text = pd.read_csv('Book1.csv', sep=',', header=None, skiprows= 100, nrows=3)
print(text[50])
I am trying to populate an empty dataframe by using the csv module to iterate over a large tab-delimited file, and replacing each row in the dataframe with these values. (Before you ask, yes I have tried all the normal read_csv methods, and nothing has worked because of dtype issues and how large the file is).
I first made an empty numpy array using np.empty, using the dimensions of my data. I then converted this to a pandas DataFrame. Then, I did the following:
with open(input_file) as csvfile:
reader = csv.DictReader(csvfile,delimiter='\t')
row_num = 0
for row in reader:
for key, value in row.items():
df.loc[row_num,key] = value
row_num += 1
This is working great, except that my file has 900,000 columns, so it is unbelievably slow. This also feels like something that pandas could do more efficiently, but I've been unable to find how. The dictionary for each row given by DictReader looks like:
{'columnName1':<value>,'columnName2':<value> ...}
Where the values are what I want to put in the dataframe in those columns for that row.
Thanks!
So what you could do in this case is to build smaller chunks of your big csv data file. I had the same issue with a 32GB Csv-File, so I had to build chunks. After reading them in you could work with them.
# read the large csv file with specified chunksize
df_chunk = pd.read_csv(r'../input/data.csv', chunksize=1000000)
chunksize=1000000 sets how many row are read in at once
Helpfull website:
https://towardsdatascience.com/why-and-how-to-use-pandas-with-large-data-9594dda2ea4c
I am reading csv files into python using:
df = pd.read_csv(r"C:\csvfile.csv")
But the file has some summary data, and the raw data start if a value "valx" is found. If "valx" is not found then the file is useless. I would like to create news dataframes that start when "valx" is found. I have been trying for a while with no success. Any help on how to achieve this is greatly appreciated.
Unfortunately, pandas only accepts skiprows for rows to skip in the beginning. You might want to parse the file before creating the dataframe.
As an example:
import csv
with open(r"C:\csvfile.csv","r") as f:
lines = csv.reader(f, newline = '')
if any('valx' in i for i in lines):
data = lines
Using the Standard Libary csv module, you can read file and check if valx is in the file, if it is found, the content will be returned in the data variable.
From there you can use the data variable to create your dataframe.
I'm trying to use it to manipulate data in large txt-files.
I have a txt-file with more than 2000 columns, and about a third of these have a title which contains the word 'Net'. I want to extract only these columns and write them to a new txt file. Any suggestion on how I can do that?
I have searched around a bit but haven't been able to find something that helps me. Apologies if similar questions have been asked and solved before.
EDIT 1: Thank you all! At the moment of writing 3 users have suggested solutions and they all work really well. I honestly didn't think people would answer so I didn't check for a day or two, and was happily surprised by this. I'm very impressed.
EDIT 2: I've added a picture that shows what a part of the original txt-file can look like, in case it will help anyone in the future:
One way of doing this, without the installation of third-party modules like numpy/pandas, is as follows. Given an input file, called "input.csv" like this:
a,b,c_net,d,e_net
0,0,1,0,1
0,0,1,0,1
(remove the blank lines in between, they are just for formatting the
content in this post)
The following code does what you want.
import csv
input_filename = 'input.csv'
output_filename = 'output.csv'
# Instantiate a CSV reader, check if you have the appropriate delimiter
reader = csv.reader(open(input_filename), delimiter=',')
# Get the first row (assuming this row contains the header)
input_header = reader.next()
# Filter out the columns that you want to keep by storing the column
# index
columns_to_keep = []
for i, name in enumerate(input_header):
if 'net' in name:
columns_to_keep.append(i)
# Create a CSV writer to store the columns you want to keep
writer = csv.writer(open(output_filename, 'w'), delimiter=',')
# Construct the header of the output file
output_header = []
for column_index in columns_to_keep:
output_header.append(input_header[column_index])
# Write the header to the output file
writer.writerow(output_header)
# Iterate of the remainder of the input file, construct a row
# with columns you want to keep and write this row to the output file
for row in reader:
new_row = []
for column_index in columns_to_keep:
new_row.append(row[column_index])
writer.writerow(new_row)
Note that there is no error handling. There are at least two that should be handled. The first one is the check for the existence of the input file (hint: check the functionality provide by the os and os.path modules). The second one is to handle blank lines or lines with an inconsistent amount of columns.
This could be done for instance with Pandas,
import pandas as pd
df = pd.read_csv('path_to_file.txt', sep='\s+')
print(df.columns) # check that the columns are parsed correctly
selected_columns = [col for col in df.columns if "net" in col]
df_filtered = df[selected_columns]
df_filtered.to_csv('new_file.txt')
Of course, since we don't have the structure of your text file, you would have to adapt the arguments of read_csv to make this work in your case (see the the corresponding documentation).
This will load all the file in memory and then filter out the unnecessary columns. If your file is so large that it cannot be loaded in RAM at once, there is a way to load only specific columns with the usecols argument.
You can use pandas filter function to select few columns based on regex
data_filtered = data.filter(regex='net')