I have data in a csv file that looks like that is imported as this.
import csv
with open('Half-life.csv', 'r') as f:
data = list(csv.reader(f))
the data will come out as this to where it prints out the rows like data[0] = ['10', '2', '2'] and so on.
What i'm wanting though is to retrieve the data as columns in instead of rows, to where in this case, there are 3 columns.
You can create three separate lists, and then append to each using csv.reader.
import csv
c1 = []
c2 = []
c3 = []
with open('Half-life.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
c1.append(row[0])
c2.append(row[1])
c3.append(row[2])
for loop could be replaced with columns = zip(*reader).A little more automatic and flexible version of Alexander's answer:
import csv
from collections import defaultdict
columns = defaultdict(list)
with open('Half-life.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
for i in range(len(row)):
columns[i].append(row[i])
# Following line is only necessary if you want a key error for invalid column numbers
columns = dict(columns)
You could also modify this to use column headers instead of column numbers.
import csv
from collections import defaultdict
columns = defaultdict(list)
with open('Half-life.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
headers = next(reader)
column_nums = range(len(headers)) # Do NOT change to xrange
for row in reader:
for i in column_nums:
columns[headers[i]].append(row[i])
# Following line is only necessary if you want a key error for invalid column names
columns = dict(columns)
Another option, if you have numpy installed, you can use loadtxt to read a csv file into a numpy array. You can then transpose the array if you want more columns than rows (I wasn't quite clear on how you wanted the data to look). For example:
import numpy as np
# Load data
data = np.loadtxt('csv_file.csv', delimiter=',')
# Transpose data if needs be
data = np.transpose(data)
pandas.read_csv('Half-life.csv')would do this and infer proper types for you.