Python import csv to list

Question

I have a CSV file with about 2000 records.

Each record has a string, and a category to it:

This is the firs         


        

Answer 1

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
dicts = df.to_dict().values()

One big advantage is that pandas deals automatically with header rows.

If you haven't heard of Seaborn, I recommend having a look at it.

See also: How do I read and write CSV files with Python?

Pandas #2

import pandas as pd

# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()

# Convert
dicts = df.to_dict('records')

The content of df is:

     country   population population_time    EUR
0    Germany   82521653.0      2016-12-01   True
1     France   66991000.0      2017-01-01   True
2  Indonesia  255461700.0      2017-01-01  False
3    Ireland    4761865.0             NaT   True
4      Spain   46549045.0      2017-06-01   True
5    Vatican          NaN             NaT   True

The content of dicts is

[{'country': 'Germany', 'population': 82521653.0, 'population_time': Timestamp('2016-12-01 00:00:00'), 'EUR': True},
 {'country': 'France', 'population': 66991000.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': True},
 {'country': 'Indonesia', 'population': 255461700.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': False},
 {'country': 'Ireland', 'population': 4761865.0, 'population_time': NaT, 'EUR': True},
 {'country': 'Spain', 'population': 46549045.0, 'population_time': Timestamp('2017-06-01 00:00:00'), 'EUR': True},
 {'country': 'Vatican', 'population': nan, 'population_time': NaT, 'EUR': True}]

Pandas #3

import pandas as pd

# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()

# Convert
lists = [[row[col] for col in df.columns] for row in df.to_dict('records')]

The content of lists is:

[['Germany', 82521653.0, Timestamp('2016-12-01 00:00:00'), True],
 ['France', 66991000.0, Timestamp('2017-01-01 00:00:00'), True],
 ['Indonesia', 255461700.0, Timestamp('2017-01-01 00:00:00'), False],
 ['Ireland', 4761865.0, NaT, True],
 ['Spain', 46549045.0, Timestamp('2017-06-01 00:00:00'), True],
 ['Vatican', nan, NaT, True]]

Answer 2

Using the csv module:

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    data = list(reader)

print(data)

Output:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

---

If you need tuples:

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    data = [tuple(row) for row in reader]

print(data)

Output:

[('This is the first line', 'Line1'), ('This is the second line', 'Line2'), ('This is the third line', 'Line3')]

---

Old Python 2 answer, also using the csv module:

import csv
with open('file.csv', 'rb') as f:
    reader = csv.reader(f)
    your_list = list(reader)

print your_list
# [['This is the first line', 'Line1'],
#  ['This is the second line', 'Line2'],
#  ['This is the third line', 'Line3']]

Answer 3

Here is the easiest way in Python 3.x to import a CSV to a multidimensional array, and its only 4 lines of code without importing anything!

#pull a CSV into a multidimensional array in 4 lines!

L=[]                            #Create an empty list for the main array
for line in open('log.txt'):    #Open the file and read all the lines
    x=line.rstrip()             #Strip the \n from each line
    L.append(x.split(','))      #Split each line into a list and add it to the
                                #Multidimensional array
print(L)

Answer 4

As said already in the comments you can use the csv library in python. csv means comma separated values which seems exactly your case: a label and a value separated by a comma.

Being a category and value type I would rather use a dictionary type instead of a list of tuples.

Anyway in the code below I show both ways: d is the dictionary and l is the list of tuples.

import csv

file_name = "test.txt"
try:
    csvfile = open(file_name, 'rt')
except:
    print("File not found")
csvReader = csv.reader(csvfile, delimiter=",")
d = dict()
l =  list()
for row in csvReader:
    d[row[1]] = row[0]
    l.append((row[0], row[1]))
print(d)
print(l)

Answer 5

Unfortunately I find none of the existing answers particularly satisfying.

Here is a straightforward and complete Python 3 solution, using the csv module.

import csv

with open('../resources/temp_in.csv', newline='') as f:
    reader = csv.reader(f, skipinitialspace=True)
    rows = list(reader)

print(rows)

Notice the skipinitialspace=True argument. This is necessary since, unfortunately, OP's CSV contains whitespace after each comma.

Output:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

Answer 6

Next is a piece of code which uses csv module but extracts file.csv contents to a list of dicts using the first line which is a header of csv table

import csv
def csv2dicts(filename):
  with open(filename, 'rb') as f:
    reader = csv.reader(f)
    lines = list(reader)
    if len(lines) < 2: return None
    names = lines[0]
    if len(names) < 1: return None
    dicts = []
    for values in lines[1:]:
      if len(values) != len(names): return None
      d = {}
      for i,_ in enumerate(names):
        d[names[i]] = values[i]
      dicts.append(d)
    return dicts
  return None

if __name__ == '__main__':
  your_list = csv2dicts('file.csv')
  print your_list