运行 MatplotLib Python 代码导入错误
Running MatplotLib Python Code ImportErrors
我正在尝试 运行 Randy Olson 的代码 - 授予女性学士学位的百分比。
http://www.randalolson.com/2014/06/28/how-to-make-beautiful-data-visualizations-in-python-with-matplotlib/
完整代码（由 Randy Olson 编写，显然不是我）：
from pandas import read_csv
# Read the data into a pandas DataFrame.
gender_degree_data = read_csv("http://www.randalolson.com/wp-content/uploads/percent-bachelors-degrees-women-usa.csv")
# These are the "Tableau 20" colors as RGB.
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.
for i in range(len(tableau20)):
r, g, b = tableau20[i]
tableau20[i] = (r / 255., g / 255., b / 255.)
# You typically want your plot to be ~1.33x wider than tall. This plot is a rare
# exception because of the number of lines being plotted on it.
# Common sizes: (10, 7.5) and (12, 9)
figure(figsize=(12, 14))
# Remove the plot frame lines. They are unnecessary chartjunk.
ax = subplot(111)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
# Ensure that the axis ticks only show up on the bottom and left of the plot.
# Ticks on the right and top of the plot are generally unnecessary chartjunk.
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
# Limit the range of the plot to only where the data is.
# Avoid unnecessary whitespace.
ylim(0, 90)
xlim(1968, 2014)
# Make sure your axis ticks are large enough to be easily read.
# You don't want your viewers squinting to read your plot.
yticks(range(0, 91, 10), [str(x) + "%" for x in range(0, 91, 10)], fontsize=14)
xticks(fontsize=14)
# Provide tick lines across the plot to help your viewers trace along
# the axis ticks. Make sure that the lines are light and small so they
# don't obscure the primary data lines.
for y in range(10, 91, 10):
plot(range(1968, 2012), [y] * len(range(1968, 2012)), "--", lw=0.5, color="black", alpha=0.3)
# Remove the tick marks; they are unnecessary with the tick lines we just plotted.
plt.tick_params(axis="both", which="both", bottom="off", top="off",
labelbottom="on", left="off", right="off", labelleft="on")
# Now that the plot is prepared, it's time to actually plot the data!
# Note that I plotted the majors in order of the highest % in the final year.
majors = ['Health Professions', 'Public Administration', 'Education', 'Psychology',
'Foreign Languages', 'English', 'Communications\nand Journalism',
'Art and Performance', 'Biology', 'Agriculture',
'Social Sciences and History', 'Business', 'Math and Statistics',
'Architecture', 'Physical Sciences', 'Computer Science',
'Engineering']
for rank, column in enumerate(majors):
# Plot each line separately with its own color, using the Tableau 20
# color set in order.
plot(gender_degree_data.Year.values,
gender_degree_data[column.replace("\n", " ")].values,
lw=2.5, color=tableau20[rank])
# Add a text label to the right end of every line. Most of the code below
# is adding specific offsets y position because some labels overlapped.
y_pos = gender_degree_data[column.replace("\n", " ")].values[-1] - 0.5
if column == "Foreign Languages":
y_pos += 0.5
elif column == "English":
y_pos -= 0.5
elif column == "Communications\nand Journalism":
y_pos += 0.75
elif column == "Art and Performance":
y_pos -= 0.25
elif column == "Agriculture":
y_pos += 1.25
elif column == "Social Sciences and History":
y_pos += 0.25
elif column == "Business":
y_pos -= 0.75
elif column == "Math and Statistics":
y_pos += 0.75
elif column == "Architecture":
y_pos -= 0.75
elif column == "Computer Science":
y_pos += 0.75
elif column == "Engineering":
y_pos -= 0.25
# Again, make sure that all labels are large enough to be easily read
# by the viewer.
text(2011.5, y_pos, column, fontsize=14, color=tableau20[rank])
# matplotlib's title() call centers the title on the plot, but not the graph,
# so I used the text() call to customize where the title goes.
# Make the title big enough so it spans the entire plot, but don't make it
# so big that it requires two lines to show.
# Note that if the title is descriptive enough, it is unnecessary to include
# axis labels; they are self-evident, in this plot's case.
text(1995, 93, "Percentage of Bachelor's degrees conferred to women in the U.S.A."
", by major (1970-2012)", fontsize=17, ha="center")
# Always include your data source(s) and copyright notice! And for your
# data sources, tell your viewers exactly where the data came from,
# preferably with a direct link to the data. Just telling your viewers
# that you used data from the "U.S. Census Bureau" is completely useless:
# the U.S. Census Bureau provides all kinds of data, so how are your
# viewers supposed to know which data set you used?
text(1966, -8, "Data source: nces.ed.gov/programs/digest/2013menu_tables.asp"
"\nAuthor: Randy Olson (randalolson.com / @randal_olson)"
"\nNote: Some majors are missing because the historical data "
"is not available for them", fontsize=10)
# Finally, save the figure as a PNG.
# You can also save it as a PDF, JPEG, etc.
# Just change the file extension in this call.
# bbox_inches="tight" removes all the extra whitespace on the edges of your plot.
savefig("percent-bachelors-degrees-women-usa.png", bbox_inches="tight");
我通过 Anaconda 安装 Python 时拥有所有依赖项。不过，我不确定如何通过 IPython Notebook 运行 它，希望我能解决这个问题。我在导入时遇到问题
我有：
from pandas import read_csv
from matplotlib import *
from matplotlib.figure import figure
但我一直收到 TypeError: 'module' object is not callable 或 ImportError: cannot import name figure
我知道这是一个非常基本的 Python 问题，但我不确定该怎么做。我想要一个包含多条线并具有交互式悬停工具的线图，这似乎是我能找到的最好的例子。如果有人知道如何解决这个问题，或者甚至知道其他易于使用新数据操作的已编写交互式线图的示例，请告诉我！
编辑：
使用
from pandas import read_csv
from matplotlib import *
from matplotlib.figure import Figure
import pandas
和相同的代码：
完整追溯
runfile('C:/Users/jbyrusb/Documents/Python Scripts/Disputes/WomenDegreesExample.py', wdir='C:/Users/jbyrusb/Documents/Python Scripts/Disputes')
Traceback (most recent call last):
File "<ipython-input-30-1b99e15a9df1>", line 1, in <module>
runfile('C:/Users/jbyrusb/Documents/Python Scripts/Disputes/WomenDegreesExample.py', wdir='C:/Users/jbyrusb/Documents/Python Scripts/Disputes')
File "C:\Users\jbyrusb\AppData\Local\Continuum\Anaconda\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 682, in runfile
execfile(filename, namespace)
File "C:\Users\jbyrusb\AppData\Local\Continuum\Anaconda\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 71, in execfile
exec(compile(scripttext, filename, 'exec'), glob, loc)
File "C:/Users/jbyrusb/Documents/Python Scripts/Disputes/WomenDegreesExample.py", line 33, in <module>
figure(figsize=(12, 14))
TypeError: 'module' object is not callable
您的 link 中的示例调用 %pylab inline，其中一个 ipython 命令执行 from pylab import *。
这实际上是演示 matplotlib 的最糟糕的方式，如果我可以挥动魔杖并将它从互联网和世界上删除，我会的。
总之，在原代码最前面加上from pylab import *应该可以解决问题
这是现代面向对象的 matplotlib 中的代码：
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas
import seaborn
seaborn.set(style='white')
# Read the data into a pandas DataFrame.
url = "http://www.randalolson.com/wp-content/uploads/percent-bachelors-degrees-women-usa.csv"
gender_degree_data = pandas.read_csv(url)
# These are the "Tableau 20" colors as RGB.
tableau20 = np.array([
( 31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
( 44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), ( 23, 190, 207), (158, 218, 229)
]) / 255.
fig, ax = plt.subplots(figsize=(12, 14))
seaborn.despine(ax=ax, left=True, bottom=True)
ax.xaxis.tick_bottom()
ax.yaxis.tick_left()
ax.set_ylim(bottom=0, top=90)
ax.set_xlim(left=1968, right=2014)
ax.set_yticks(range(0, 91, 10))
ax.set_yticklabels([str(x) + "%" for x in range(0, 91, 10)])
for y in range(10, 91, 10):
ax.plot(range(1968, 2012), [y] * len(range(1968, 2012)), "--",
lw=0.5, color="black", alpha=0.3)
ax.tick_params(axis="both", which="both", bottom="off", top="off", labelsize=14,
labelbottom="on", left="off", right="off", labelleft="on")
majors = [
'Health Professions', 'Public Administration', 'Education',
'Psychology','Foreign Languages', 'English',
'Communications\nand Journalism', 'Art and Performance',
'Biology', 'Agriculture', 'Social Sciences and History',
'Business', 'Math and Statistics', 'Architecture',
'Physical Sciences', 'Computer Science','Engineering'
]
offsets = {
"Foreign Languages": +0.5,
"English": -0.5,
"Communications\nand Journalism": +0.75,
"Art and Performance": -0.25,
"Agriculture": +1.25,
"Social Sciences and History": +0.25,
"Business": -0.75,
"Math and Statistics": +0.75,
"Architecture": -0.75,
"Computer Science": +0.75,
"Engineering": -0.25,
}
for rank, column in enumerate(majors):
ax.plot(gender_degree_data.Year.values,
gender_degree_data[column.replace("\n", " ")].values,
lw=2.5, color=tableau20[rank])
y_pos = gender_degree_data[column.replace("\n", " ")].values[-1] - 0.5
y_pos += offsets.get(column, 0)
ax.text(2011.5, y_pos, column, fontsize=14, color=tableau20[rank])
ax.text(1995, 93, "Percentage of Bachelor's degrees conferred to women in the U.S.A."
", by major (1970-2012)", fontsize=17, ha="center")
ax.text(1966, -8, "Data source: nces.ed.gov/programs/digest/2013menu_tables.asp"
"\nAuthor: Randy Olson (randalolson.com / @randal_olson)"
"\nNote: Some majors are missing because the historical data "
"is not available for them", fontsize=10)
fig.savefig("percent-bachelors-degrees-women-usa.png", bbox_inches="tight")
正如 Paul 指出的那样，使用 %pylab inline 是一种过时的做法，不应再使用。这是更新后的代码，可以 运行 在 IPython Notebook 之外，并且不会添加额外的 Seaborn 依赖项。
我还编写了一个仅使用 matplotlib 的示例。您可以在 matplotlib 库中找到它 here.
import matplotlib.pyplot as plt
import pandas as pd
# Read the data into a pandas DataFrame.
gender_degree_data = pd.read_csv("http://www.randalolson.com/wp-content/uploads/percent-bachelors-degrees-women-usa.csv")
# These are the "Tableau 20" colors as RGB.
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.
for i in range(len(tableau20)):
r, g, b = tableau20[i]
tableau20[i] = (r / 255., g / 255., b / 255.)
# You typically want your plot to be ~1.33x wider than tall. This plot is a rare
# exception because of the number of lines being plotted on it.
# Common sizes: (10, 7.5) and (12, 9)
plt.figure(figsize=(12, 14))
# Remove the plot frame lines. They are unnecessary chartjunk.
ax = plt.subplot(111)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
# Ensure that the axis ticks only show up on the bottom and left of the plot.
# Ticks on the right and top of the plot are generally unnecessary chartjunk.
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
# Limit the range of the plot to only where the data is.
# Avoid unnecessary whitespace.
plt.ylim(0, 90)
plt.xlim(1968, 2014)
# Make sure your axis ticks are large enough to be easily read.
# You don't want your viewers squinting to read your plot.
plt.yticks(range(0, 91, 10), [str(x) + "%" for x in range(0, 91, 10)], fontsize=14)
plt.xticks(fontsize=14)
# Provide tick lines across the plot to help your viewers trace along
# the axis ticks. Make sure that the lines are light and small so they
# don't obscure the primary data lines.
for y in range(10, 91, 10):
plt.plot(range(1968, 2012), [y] * len(range(1968, 2012)), "--", lw=0.5, color="black", alpha=0.3)
# Remove the tick marks; they are unnecessary with the tick lines we just plotted.
plt.tick_params(axis="both", which="both", bottom="off", top="off",
labelbottom="on", left="off", right="off", labelleft="on")
# Now that the plot is prepared, it's time to actually plot the data!
# Note that I plotted the majors in order of the highest % in the final year.
majors = ['Health Professions', 'Public Administration', 'Education', 'Psychology',
'Foreign Languages', 'English', 'Communications\nand Journalism',
'Art and Performance', 'Biology', 'Agriculture',
'Social Sciences and History', 'Business', 'Math and Statistics',
'Architecture', 'Physical Sciences', 'Computer Science',
'Engineering']
for rank, column in enumerate(majors):
# Plot each line separately with its own color, using the Tableau 20
# color set in order.
plt.plot(gender_degree_data.Year.values,
gender_degree_data[column.replace("\n", " ")].values,
lw=2.5, color=tableau20[rank])
# Add a text label to the right end of every line. Most of the code below
# is adding specific offsets y position because some labels overlapped.
y_pos = gender_degree_data[column.replace("\n", " ")].values[-1] - 0.5
if column == "Foreign Languages":
y_pos += 0.5
elif column == "English":
y_pos -= 0.5
elif column == "Communications\nand Journalism":
y_pos += 0.75
elif column == "Art and Performance":
y_pos -= 0.25
elif column == "Agriculture":
y_pos += 1.25
elif column == "Social Sciences and History":
y_pos += 0.25
elif column == "Business":
y_pos -= 0.75
elif column == "Math and Statistics":
y_pos += 0.75
elif column == "Architecture":
y_pos -= 0.75
elif column == "Computer Science":
y_pos += 0.75
elif column == "Engineering":
y_pos -= 0.25
# Again, make sure that all labels are large enough to be easily read
# by the viewer.
plt.text(2011.5, y_pos, column, fontsize=14, color=tableau20[rank])
# matplotlib's title() call centers the title on the plot, but not the graph,
# so I used the text() call to customize where the title goes.
# Make the title big enough so it spans the entire plot, but don't make it
# so big that it requires two lines to show.
# Note that if the title is descriptive enough, it is unnecessary to include
# axis labels; they are self-evident, in this plot's case.
plt.text(1995, 93, "Percentage of Bachelor's degrees conferred to women in the U.S.A."
", by major (1970-2012)", fontsize=17, ha="center")
# Always include your data source(s) and copyright notice! And for your
# data sources, tell your viewers exactly where the data came from,
# preferably with a direct link to the data. Just telling your viewers
# that you used data from the "U.S. Census Bureau" is completely useless:
# the U.S. Census Bureau provides all kinds of data, so how are your
# viewers supposed to know which data set you used?
plt.text(1966, -8, "Data source: nces.ed.gov/programs/digest/2013menu_tables.asp"
"\nAuthor: Randy Olson (randalolson.com / @randal_olson)"
"\nNote: Some majors are missing because the historical data "
"is not available for them", fontsize=10)
# Finally, save the figure as a PNG.
# You can also save it as a PDF, JPEG, etc.
# Just change the file extension in this call.
# bbox_inches="tight" removes all the extra whitespace on the edges of your plot.
plt.savefig("percent-bachelors-degrees-women-usa.png", bbox_inches="tight");
最终结果如下所示：
我也用这个新代码更新了我的 blog post。感谢您提醒我注意这个问题！
我正在尝试 运行 Randy Olson 的代码 - 授予女性学士学位的百分比。 http://www.randalolson.com/2014/06/28/how-to-make-beautiful-data-visualizations-in-python-with-matplotlib/
完整代码（由 Randy Olson 编写，显然不是我）：
from pandas import read_csv
# Read the data into a pandas DataFrame.
gender_degree_data = read_csv("http://www.randalolson.com/wp-content/uploads/percent-bachelors-degrees-women-usa.csv")
# These are the "Tableau 20" colors as RGB.
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.
for i in range(len(tableau20)):
r, g, b = tableau20[i]
tableau20[i] = (r / 255., g / 255., b / 255.)
# You typically want your plot to be ~1.33x wider than tall. This plot is a rare
# exception because of the number of lines being plotted on it.
# Common sizes: (10, 7.5) and (12, 9)
figure(figsize=(12, 14))
# Remove the plot frame lines. They are unnecessary chartjunk.
ax = subplot(111)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
# Ensure that the axis ticks only show up on the bottom and left of the plot.
# Ticks on the right and top of the plot are generally unnecessary chartjunk.
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
# Limit the range of the plot to only where the data is.
# Avoid unnecessary whitespace.
ylim(0, 90)
xlim(1968, 2014)
# Make sure your axis ticks are large enough to be easily read.
# You don't want your viewers squinting to read your plot.
yticks(range(0, 91, 10), [str(x) + "%" for x in range(0, 91, 10)], fontsize=14)
xticks(fontsize=14)
# Provide tick lines across the plot to help your viewers trace along
# the axis ticks. Make sure that the lines are light and small so they
# don't obscure the primary data lines.
for y in range(10, 91, 10):
plot(range(1968, 2012), [y] * len(range(1968, 2012)), "--", lw=0.5, color="black", alpha=0.3)
# Remove the tick marks; they are unnecessary with the tick lines we just plotted.
plt.tick_params(axis="both", which="both", bottom="off", top="off",
labelbottom="on", left="off", right="off", labelleft="on")
# Now that the plot is prepared, it's time to actually plot the data!
# Note that I plotted the majors in order of the highest % in the final year.
majors = ['Health Professions', 'Public Administration', 'Education', 'Psychology',
'Foreign Languages', 'English', 'Communications\nand Journalism',
'Art and Performance', 'Biology', 'Agriculture',
'Social Sciences and History', 'Business', 'Math and Statistics',
'Architecture', 'Physical Sciences', 'Computer Science',
'Engineering']
for rank, column in enumerate(majors):
# Plot each line separately with its own color, using the Tableau 20
# color set in order.
plot(gender_degree_data.Year.values,
gender_degree_data[column.replace("\n", " ")].values,
lw=2.5, color=tableau20[rank])
# Add a text label to the right end of every line. Most of the code below
# is adding specific offsets y position because some labels overlapped.
y_pos = gender_degree_data[column.replace("\n", " ")].values[-1] - 0.5
if column == "Foreign Languages":
y_pos += 0.5
elif column == "English":
y_pos -= 0.5
elif column == "Communications\nand Journalism":
y_pos += 0.75
elif column == "Art and Performance":
y_pos -= 0.25
elif column == "Agriculture":
y_pos += 1.25
elif column == "Social Sciences and History":
y_pos += 0.25
elif column == "Business":
y_pos -= 0.75
elif column == "Math and Statistics":
y_pos += 0.75
elif column == "Architecture":
y_pos -= 0.75
elif column == "Computer Science":
y_pos += 0.75
elif column == "Engineering":
y_pos -= 0.25
# Again, make sure that all labels are large enough to be easily read
# by the viewer.
text(2011.5, y_pos, column, fontsize=14, color=tableau20[rank])
# matplotlib's title() call centers the title on the plot, but not the graph,
# so I used the text() call to customize where the title goes.
# Make the title big enough so it spans the entire plot, but don't make it
# so big that it requires two lines to show.
# Note that if the title is descriptive enough, it is unnecessary to include
# axis labels; they are self-evident, in this plot's case.
text(1995, 93, "Percentage of Bachelor's degrees conferred to women in the U.S.A."
", by major (1970-2012)", fontsize=17, ha="center")
# Always include your data source(s) and copyright notice! And for your
# data sources, tell your viewers exactly where the data came from,
# preferably with a direct link to the data. Just telling your viewers
# that you used data from the "U.S. Census Bureau" is completely useless:
# the U.S. Census Bureau provides all kinds of data, so how are your
# viewers supposed to know which data set you used?
text(1966, -8, "Data source: nces.ed.gov/programs/digest/2013menu_tables.asp"
"\nAuthor: Randy Olson (randalolson.com / @randal_olson)"
"\nNote: Some majors are missing because the historical data "
"is not available for them", fontsize=10)
# Finally, save the figure as a PNG.
# You can also save it as a PDF, JPEG, etc.
# Just change the file extension in this call.
# bbox_inches="tight" removes all the extra whitespace on the edges of your plot.
savefig("percent-bachelors-degrees-women-usa.png", bbox_inches="tight");
我通过 Anaconda 安装 Python 时拥有所有依赖项。不过，我不确定如何通过 IPython Notebook 运行 它，希望我能解决这个问题。我在导入时遇到问题 我有：
from pandas import read_csv
from matplotlib import *
from matplotlib.figure import figure
但我一直收到 TypeError: 'module' object is not callable 或 ImportError: cannot import name figure
我知道这是一个非常基本的 Python 问题，但我不确定该怎么做。我想要一个包含多条线并具有交互式悬停工具的线图，这似乎是我能找到的最好的例子。如果有人知道如何解决这个问题，或者甚至知道其他易于使用新数据操作的已编写交互式线图的示例，请告诉我！
编辑：
使用
from pandas import read_csv
from matplotlib import *
from matplotlib.figure import Figure
import pandas
和相同的代码：
完整追溯
runfile('C:/Users/jbyrusb/Documents/Python Scripts/Disputes/WomenDegreesExample.py', wdir='C:/Users/jbyrusb/Documents/Python Scripts/Disputes')
Traceback (most recent call last):
File "<ipython-input-30-1b99e15a9df1>", line 1, in <module>
runfile('C:/Users/jbyrusb/Documents/Python Scripts/Disputes/WomenDegreesExample.py', wdir='C:/Users/jbyrusb/Documents/Python Scripts/Disputes')
File "C:\Users\jbyrusb\AppData\Local\Continuum\Anaconda\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 682, in runfile
execfile(filename, namespace)
File "C:\Users\jbyrusb\AppData\Local\Continuum\Anaconda\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 71, in execfile
exec(compile(scripttext, filename, 'exec'), glob, loc)
File "C:/Users/jbyrusb/Documents/Python Scripts/Disputes/WomenDegreesExample.py", line 33, in <module>
figure(figsize=(12, 14))
TypeError: 'module' object is not callable
您的 link 中的示例调用 %pylab inline，其中一个 ipython 命令执行 from pylab import *。
这实际上是演示 matplotlib 的最糟糕的方式，如果我可以挥动魔杖并将它从互联网和世界上删除，我会的。
总之，在原代码最前面加上from pylab import *应该可以解决问题
这是现代面向对象的 matplotlib 中的代码：
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas
import seaborn
seaborn.set(style='white')
# Read the data into a pandas DataFrame.
url = "http://www.randalolson.com/wp-content/uploads/percent-bachelors-degrees-women-usa.csv"
gender_degree_data = pandas.read_csv(url)
# These are the "Tableau 20" colors as RGB.
tableau20 = np.array([
( 31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
( 44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), ( 23, 190, 207), (158, 218, 229)
]) / 255.
fig, ax = plt.subplots(figsize=(12, 14))
seaborn.despine(ax=ax, left=True, bottom=True)
ax.xaxis.tick_bottom()
ax.yaxis.tick_left()
ax.set_ylim(bottom=0, top=90)
ax.set_xlim(left=1968, right=2014)
ax.set_yticks(range(0, 91, 10))
ax.set_yticklabels([str(x) + "%" for x in range(0, 91, 10)])
for y in range(10, 91, 10):
ax.plot(range(1968, 2012), [y] * len(range(1968, 2012)), "--",
lw=0.5, color="black", alpha=0.3)
ax.tick_params(axis="both", which="both", bottom="off", top="off", labelsize=14,
labelbottom="on", left="off", right="off", labelleft="on")
majors = [
'Health Professions', 'Public Administration', 'Education',
'Psychology','Foreign Languages', 'English',
'Communications\nand Journalism', 'Art and Performance',
'Biology', 'Agriculture', 'Social Sciences and History',
'Business', 'Math and Statistics', 'Architecture',
'Physical Sciences', 'Computer Science','Engineering'
]
offsets = {
"Foreign Languages": +0.5,
"English": -0.5,
"Communications\nand Journalism": +0.75,
"Art and Performance": -0.25,
"Agriculture": +1.25,
"Social Sciences and History": +0.25,
"Business": -0.75,
"Math and Statistics": +0.75,
"Architecture": -0.75,
"Computer Science": +0.75,
"Engineering": -0.25,
}
for rank, column in enumerate(majors):
ax.plot(gender_degree_data.Year.values,
gender_degree_data[column.replace("\n", " ")].values,
lw=2.5, color=tableau20[rank])
y_pos = gender_degree_data[column.replace("\n", " ")].values[-1] - 0.5
y_pos += offsets.get(column, 0)
ax.text(2011.5, y_pos, column, fontsize=14, color=tableau20[rank])
ax.text(1995, 93, "Percentage of Bachelor's degrees conferred to women in the U.S.A."
", by major (1970-2012)", fontsize=17, ha="center")
ax.text(1966, -8, "Data source: nces.ed.gov/programs/digest/2013menu_tables.asp"
"\nAuthor: Randy Olson (randalolson.com / @randal_olson)"
"\nNote: Some majors are missing because the historical data "
"is not available for them", fontsize=10)
fig.savefig("percent-bachelors-degrees-women-usa.png", bbox_inches="tight")
正如 Paul 指出的那样，使用 %pylab inline 是一种过时的做法，不应再使用。这是更新后的代码，可以 运行 在 IPython Notebook 之外，并且不会添加额外的 Seaborn 依赖项。
我还编写了一个仅使用 matplotlib 的示例。您可以在 matplotlib 库中找到它 here.
import matplotlib.pyplot as plt
import pandas as pd
# Read the data into a pandas DataFrame.
gender_degree_data = pd.read_csv("http://www.randalolson.com/wp-content/uploads/percent-bachelors-degrees-women-usa.csv")
# These are the "Tableau 20" colors as RGB.
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.
for i in range(len(tableau20)):
r, g, b = tableau20[i]
tableau20[i] = (r / 255., g / 255., b / 255.)
# You typically want your plot to be ~1.33x wider than tall. This plot is a rare
# exception because of the number of lines being plotted on it.
# Common sizes: (10, 7.5) and (12, 9)
plt.figure(figsize=(12, 14))
# Remove the plot frame lines. They are unnecessary chartjunk.
ax = plt.subplot(111)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
# Ensure that the axis ticks only show up on the bottom and left of the plot.
# Ticks on the right and top of the plot are generally unnecessary chartjunk.
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
# Limit the range of the plot to only where the data is.
# Avoid unnecessary whitespace.
plt.ylim(0, 90)
plt.xlim(1968, 2014)
# Make sure your axis ticks are large enough to be easily read.
# You don't want your viewers squinting to read your plot.
plt.yticks(range(0, 91, 10), [str(x) + "%" for x in range(0, 91, 10)], fontsize=14)
plt.xticks(fontsize=14)
# Provide tick lines across the plot to help your viewers trace along
# the axis ticks. Make sure that the lines are light and small so they
# don't obscure the primary data lines.
for y in range(10, 91, 10):
plt.plot(range(1968, 2012), [y] * len(range(1968, 2012)), "--", lw=0.5, color="black", alpha=0.3)
# Remove the tick marks; they are unnecessary with the tick lines we just plotted.
plt.tick_params(axis="both", which="both", bottom="off", top="off",
labelbottom="on", left="off", right="off", labelleft="on")
# Now that the plot is prepared, it's time to actually plot the data!
# Note that I plotted the majors in order of the highest % in the final year.
majors = ['Health Professions', 'Public Administration', 'Education', 'Psychology',
'Foreign Languages', 'English', 'Communications\nand Journalism',
'Art and Performance', 'Biology', 'Agriculture',
'Social Sciences and History', 'Business', 'Math and Statistics',
'Architecture', 'Physical Sciences', 'Computer Science',
'Engineering']
for rank, column in enumerate(majors):
# Plot each line separately with its own color, using the Tableau 20
# color set in order.
plt.plot(gender_degree_data.Year.values,
gender_degree_data[column.replace("\n", " ")].values,
lw=2.5, color=tableau20[rank])
# Add a text label to the right end of every line. Most of the code below
# is adding specific offsets y position because some labels overlapped.
y_pos = gender_degree_data[column.replace("\n", " ")].values[-1] - 0.5
if column == "Foreign Languages":
y_pos += 0.5
elif column == "English":
y_pos -= 0.5
elif column == "Communications\nand Journalism":
y_pos += 0.75
elif column == "Art and Performance":
y_pos -= 0.25
elif column == "Agriculture":
y_pos += 1.25
elif column == "Social Sciences and History":
y_pos += 0.25
elif column == "Business":
y_pos -= 0.75
elif column == "Math and Statistics":
y_pos += 0.75
elif column == "Architecture":
y_pos -= 0.75
elif column == "Computer Science":
y_pos += 0.75
elif column == "Engineering":
y_pos -= 0.25
# Again, make sure that all labels are large enough to be easily read
# by the viewer.
plt.text(2011.5, y_pos, column, fontsize=14, color=tableau20[rank])
# matplotlib's title() call centers the title on the plot, but not the graph,
# so I used the text() call to customize where the title goes.
# Make the title big enough so it spans the entire plot, but don't make it
# so big that it requires two lines to show.
# Note that if the title is descriptive enough, it is unnecessary to include
# axis labels; they are self-evident, in this plot's case.
plt.text(1995, 93, "Percentage of Bachelor's degrees conferred to women in the U.S.A."
", by major (1970-2012)", fontsize=17, ha="center")
# Always include your data source(s) and copyright notice! And for your
# data sources, tell your viewers exactly where the data came from,
# preferably with a direct link to the data. Just telling your viewers
# that you used data from the "U.S. Census Bureau" is completely useless:
# the U.S. Census Bureau provides all kinds of data, so how are your
# viewers supposed to know which data set you used?
plt.text(1966, -8, "Data source: nces.ed.gov/programs/digest/2013menu_tables.asp"
"\nAuthor: Randy Olson (randalolson.com / @randal_olson)"
"\nNote: Some majors are missing because the historical data "
"is not available for them", fontsize=10)
# Finally, save the figure as a PNG.
# You can also save it as a PDF, JPEG, etc.
# Just change the file extension in this call.
# bbox_inches="tight" removes all the extra whitespace on the edges of your plot.
plt.savefig("percent-bachelors-degrees-women-usa.png", bbox_inches="tight");
最终结果如下所示：
我也用这个新代码更新了我的 blog post。感谢您提醒我注意这个问题！