如何使用 Python 绘制这些数据？

Question

I need to create a bar graph of the unemployment rate for every month since 1948 using python. I also need to use a comma delimited file (.csv) but the file contains multiple commas and I'm unsure of how to use the split command to separate each variable. Below I have pasted my code so far and the file I need to split and sort. I'm unsure of where to go from here, and any advice would be greatly appreciated. Thanks.

1948,3.4,3.8,4.0,3.9,3.5,3.6,3.6,3.9,3.8,3.7,3.8,4.0 1949,4.3,4.7,5.0,5.3,6.1,6.2,6.7,6.8,6.6,7.9,6.4,6.6 1950,6.5,6.4,6.3,5.8,5.5,5.4,5.0,4.5,4.4,4.2,4.2,4.3 1951,3.7,3.4,3.4,3.1,3.0,3.2,3.1,3.1,3.3,3.5,3.5,3.1 1952,3.2,3.1,2.9,2.9,3.0,3.0,3.2,3.4,3.1,3.0,2.8,2.7 1953,2.9,2.6,2.6,2.7,2.5,2.5,2.6,2.7,2.9,3.1,3.5,4.5 1954,4.9,5.2,5.7,5.9,5.9,5.6,5.8,6.0,6.1,5.7,5.3,5.0 1955,4.9,4.7,4.6,4.7,4.3,4.2,4.0,4.2,4.1,4.3,4.2,4.2 1956,4.0,3.9,4.2,4.0,4.3,4.3,4.4,4.1,3.9,3.9,4.3,4.2 1957,4.2,3.9,3.7,3.9,4.1,4.3,4.2,4.1,4.4,4.5,5.1,5.2 1958,5.8,6.4,6.7,7.4,7.4,7.3,7.5,7.4,7.1,6.7,6.2,6.2 1959,6.0,5.9,5.6,5.2,5.1,5.0,5.1,5.2,5.5,5.7,5.8,5.3 1960,5.2,4.8,5.4,5.2,5.1,5.4,5.5,5.6,5.5,6.1,6.1,6.6 1961,6.6,6.9,6.9,7.0,7.1,6.9,7.0,6.6,6.7,6.5,6.1,6.0 1962,5.8,5.5,5.6,5.6,5.5,5.5,5.4,5.7,5.6,5.4,5.7,5.5 1963,5.7,5.9,5.7,5.7,5.9,5.6,5.6,5.4,5.5,5.5,5.7,5.5 1964,5.6,5.4,5.4,5.3,5.1,5.2,4.9,5.0,5.1,5.1,4.8,5.0 1965,4.9,5.1,4.7,4.8,4.6,4.6,4.4,4.4,4.3,4.2,4.1,4.0 1966,4.0,3.8,3.8,3.8,3.9,3.8,3.8,3.8,3.7,3.7,3 .6,3.8 1967,3.9,3.8,3.8,3.8,3.8,3.9,3.8,3.8,3.8,4.0,3.9,3.8 1968,3.7,3.8,3.7,3.5,3.5,3.7,3.7,3.5,3.4,3.4,3.4,3.4 1969,3.4,3.4,3.4,3.4,3.4,3.5,3.5,3.5,3.7,3.7,3.5,3.5 1970,3.9,4.2,4.4,4.6,4.8,4.9,5.0,5.1,5.4,5.5,5.9,6.1 1971,5.9,5.9,6.0,5.9,5.9,5.9,6.0,6.1,6.0,5.8,6.0,6.0 1972,5.8,5.7,5.8,5.7,5.7,5.7,5.6,5.6,5.5,5.6,5.3,5.2 1973,4.9,5.0,4.9,5.0,4.9,4.9,4.8,4.8,4.8,4.6,4.8,4.9 1974,5.1,5.2,5.1,5.1,5.1,5.4,5.5,5.5,5.9,6.0,6.6,7.2 1975,8.1,8.1,8.6,8.8,9.0,8.8,8.6,8.4,8.4,8.4,8.3,8.2 1976,7.9,7.7,7.6,7.7,7.4,7.6,7.8,7.8,7.6,7.7,7.8,7.8 1977,7.5,7.6,7.4,7.2,7.0,7.2,6.9,7.0,6.8,6.8,6.8,6.4 1978,6.4,6.3,6.3,6.1,6.0,5.9,6.2,5.9,6.0,5.8,5.9,6.0 1979,5.9,5.9,5.8,5.8,5.6,5.7,5.7,6.0,5.9,6.0,5.9,6.0 1980,6.3,6.3,6.3,6.9,7.5,7.6,7.8,7.7,7.5,7.5,7.5,7.2 1981,7.5,7.4,7.4,7.2,7.5,7.5,7.2,7.4,7.6,7.9,8.3,8.5 1982,8.6,8.9,9.0,9.3,9.4,9.6,9.8,9.8,10.1,10.4,10.8,10.8 1983,10.4,10.4,10.3,10.2,10.1,10.1,9.4,9.5,9.2,8.8,8.5,8.3 1984,8.0,7.8,7.8,7.7,7.4,7.2,7.5,7.5,7.3,7.4,7.2,7.3 1985,7.3,7.2,7.2,7.3,7.2,7.4, 7.4,7.1,7.1,7.1,7.0,7.0 1986,6.7,7.2,7.2,7.1,7.2,7.2,7.0,6.9,7.0,7.0,6.9,6.6 1987,6.6,6.6,6.6,6.3,6.3,6.2,6.1,6.0,5.9,6.0,5.8,5.7 1988,5.7,5.7,5.7,5.4,5.6,5.4,5.4,5.6,5.4,5.4,5.3,5.3 1989,5.4,5.2,5.0,5.2,5.2,5.3,5.2,5.2,5.3,5.3,5.4,5.4 1990,5.4,5.3,5.2,5.4,5.4,5.2,5.5,5.7,5.9,5.9,6.2,6.3 1991,6.4,6.6,6.8,6.7,6.9,6.9,6.8,6.9,6.9,7.0,7.0,7.3 1992,7.3,7.4,7.4,7.4,7.6,7.8,7.7,7.6,7.6,7.3,7.4,7.4 1993,7.3,7.1,7.0,7.1,7.1,7.0,6.9,6.8,6.7,6.8,6.6,6.5 1994,6.6,6.6,6.5,6.4,6.1,6.1,6.1,6.0,5.9,5.8,5.6,5.5 1995,5.6,5.4,5.4,5.8,5.6,5.6,5.7,5.7,5.6,5.5,5.6,5.6 1996,5.6,5.5,5.5,5.6,5.6,5.3,5.5,5.1,5.2,5.2,5.4,5.4 1997,5.3,5.2,5.2,5.1,4.9,5.0,4.9,4.8,4.9,4.7,4.6,4.7 1998,4.6,4.6,4.7,4.3,4.4,4.5,4.5,4.5,4.6,4.5,4.4,4.4 1999,4.3,4.4,4.2,4.3,4.2,4.3,4.3,4.2,4.2,4.1,4.1,4.0 2000,4.0,4.1,4.0,3.8,4.0,4.0,4.0,4.1,3.9,3.9,3.9,3.9 2001,4.2,4.2,4.3,4.4,4.3,4.5,4.6,4.9,5.0,5.3,5.5,5.7 2002,5.7,5.7,5.7,5.9,5.8,5.8,5.8,5.7,5.7,5.7,5.9,6.0 2003,5.8,5.9,5.9,6.0,6.1,6.3,6.2,6.1,6.1,6.0,5.8,5.7 2004,5.7,5.6,5.8,5.6,5 .6,5.6,5.5,5.4,5.4,5.5,5.4,5.4 2005,5.3,5.4,5.2,5.2,5.1,5.0,5.0,4.9,5.0,5.0,5.0,4.9 2006,4.7,4.8,4.7,4.7,4.6,4.6,4.7,4.7,4.5,4.4,4.5,4.4 2007,4.6,4.5,4.4,4.5,4.4,4.6,4.7,4.6,4.7,4.7,4.7,5.0 2008,5.0,4.9,5.1,5.0,5.4,5.6,5.8,6.1,6.1,6.5,6.8,7.3 2009,7.8,8.3,8.7,9.0,9.4,9.5,9.5,9.6,9.8,10.0,9.9,9.9 2010,9.8,9.8,9.9,9.9,9.6,9.4,9.4,9.5,9.5,9.4,9.8,9.3 2011,9.1,9.0,9.0,9.1,9.0,9.1,9.0,9.0,9.0,8.8,8.6,8.5 2012,8.3,8.3,8.2,8.2,8.2,8.2,8.2,8.1,7.8,7.8,7.7,7.9 2013,8.0,7.7,7.5,7.6,7.5,7.5,7.3,7.3,7.2,7.2,6.9,6.7 2014,6.6,6.7,6.7,6.2,6.3,6.1,6.2,6.2,5.9,5.7,5.8,5.6 2015,5.7,5.5,5.4,5.4,5.5,5.3,5.2,5.1,5.0,5.0,5.0,5.0 2016,4.9,4.9,5.0,5.0,4.7,4.9,4.9,4.9,4.9,4.8,4.6,4.7

2017,4.8,4.7,4.5,4.4,4.3,4.4,4.3,4.4,4.2, , ,

import matplotlib.pyplot as plt

f_in = ('unemployment.csv', 'rt')
X_labels = []
Y = []
for line in f_in:
    line = line.strip()
    month, year = line.split(',')
    X_labels.append(month)
    Y.append(int(year))
f_in.close()
X = list(range(1, len(Y) + 1))
plt.bar(X, Y, tick_label = X_labels, align = 'center')
plt.xlabel('Month')
plt.ylabel('Year')
plt.grid(True)
plt.show()

Answer 1

I would start by using Numpy to read the data, which is far easier than processing the file manually. A small example using a subset of your data:

import numpy as np
import matplotlib.pylab as pl

# Load file with Numpy as 2D array
f = np.loadtxt('file.txt', delimiter=',')

# Slice array (remove year column), and reshape to 1D
data = f[:,1:].reshape(-1)    

# Plot!
pl.figure()
pl.bar(np.arange(data.size), data)

Or, to put something more useful on the x-axis:

import numpy as np
import matplotlib.pylab as pl

# Load file with Numpy as 2D array
f = np.loadtxt('file.txt', delimiter=',')

# Slice array (remove year column), and reshape to 1D
years = f[:,0].astype(np.int)
data  = f[:,1:].reshape(-1)    

# Plot!
pl.figure()
pl.bar(np.arange(data.size), data)

# Set years as x-labels
ax=pl.gca()
ax.set_xticks(np.arange(0,data.size,12))
ax.set_xticklabels(years)