如何从csv文件制作直方图，该文件在python中只包含一列数字？

Question

I have a csv file (excel spreadsheet) of a column of roughly a million numbers. I want to make a histogram of this data with the frequency of the numbers on the y-axis and the number quantities on the x-axis. I know matplotlib can plot a histogram, but my main problem is converting the csv file from string to float since a string can't be graphed. This is what I have:

import matplotlib.pyplot as plt
import csv

with open('D1.csv', 'rb') as data:
    rows = csv.reader(data, quoting = csv.QUOTE_NONNUMERIC) 
    floats = [[item for number, item in enumerate(row) if item and (1 <= number <= 12)] for row in rows]
plt.hist(floats, bins=50)
plt.title("histogram")
plt.xlabel("value")
plt.ylabel("frequency")
plt.show()

Answer 1

You can do it in one line with pandas :

import pandas as pd

pd.read_csv('D1.csv', quoting=2)['column_you_want'].hist(bins=50)

Answer 2

Okay I finally got something to work with headings, titles, etc.

import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('D1.csv', quoting=2)
data.hist(bins=50)
plt.xlim([0,115000])
plt.title("Data")
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.show()

My first problem was that matplotlib is necessary to actually show the graph. Also, I needed to set the action

pd.read_csv('D1.csv', quoting=2)

to data so I could plot the histogram of that action with

data.hist

Thank you all for the help.

Answer 3

Panda's read_csv is very powerful, but if your csv file is simple (without headers, or NaNs or comments) you do not need Pandas, as you can use Numpy:

import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('D1.csv')
plt.hist(data, normed=True, bins='auto')

(In fact loadtxt can deal with some headers and comments, but read_csv is more versatile)