Python import csv to list

Python import csv to list

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]]

Updated for Python 3:

import csv

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

print(your_list)

Output:

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

Python import csv to list

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 havent 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]]