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def read_csv(csv_file): data = [] with open(csv_file, 'r') as f: # create a list of rows in the CSV file rows = f.readlines() # strip white-space and newlines rows = list(map(lambda x:x.strip(), rows)) for row in rows: # further split each row into columns assuming delimiter is comma row = row.split(',') # append to data-frame our new row ...
34. To read a CSV file as a pandas DataFrame, you'll need to use pd.read_csv, which has sep=',' as the default. But this isn't where the story ends; data exists in many different formats and is stored in different ways so you will often need to pass additional parameters to read_csv to ensure your data is read in properly.
I have a CSV file, here is a sample of what it looks like: Year: Dec: Jan: 1 50 60 2 25 50 3 30 30 4 40 20 5 10 10 I know how to read the file in and print each
To convert CSV to JSON in Python, follow these steps: Initialize a Python List. Read the lines of CSV file using csv.DictReader() function. Convert each line into a dictionary. Add the dictionary to the Python List created in step 1. Convert the Python List to JSON String using json.dumps().
when you want first N columns. If you don't know the column names but you want first N columns from dataframe. You can do it by. import pandas as pd. df = pd.read_csv("sample.csv", usecols=[i for i in range(n)]) Edit. When you know name of the column to be dropped. # Read column names from file.
Pandas is pretty good at dealing with data. Here is one example how to use it: import pandas as pd # Read the CSV into a pandas data frame (df) # With a df you can do many things # most important: visualize data with Seaborn df = pd.read_csv('filename.csv', delimiter=',') # Or export it in many ways, e.g. a list of tuples tuples = [tuple(x) for x in df.values] # or export it as a list of dicts ...
Reader and writer initiation take a file object. The file objects needs to have been instantiated with newline='' in the init: import csv. with open("my_file.csv", newline='') as csvfile: reader_object = csv.reader(csvfile) writer_object = csv.writer(csvfile) Reader. Iterates the file line by line.
4. From .read_csv() sep:str, default ‘,’ : Delimiter to use. ... In addition, separators longer than 1 character and different from '\s+' will be interpreted as regular expressions and will also force the use of the Python parsing engine. And | is special char in regex grammar (means OR) so you need to escape it, so you need.
Note that if you're on Python 2, you should see e.g. Python, Encoding output to UTF-8 and Convert UTF-8 with BOM to UTF-8 with no BOM in Python. You'll need to do some shenanigans with codecs or with str.decode for this to work right in Python 2. But in Python 3, all you need to do is set the encoding= parameter when you open the file.
Looking at the documentation for the Pandas read_csv() function, I see it has an encoding parameter, which should be the name of the encoding you expect that CSV file to be in. So try adding encoding="cp1252" to your read_csv() call, as follows: df = pd.read_csv(r"D:\ss.csv", encoding="cp1252")