使用Python有效地以csv格式绘制表格 [英] Efficiently ploting a table in csv format using Python
问题描述
我正在尝试使用Python绘制csv格式的表.到目前为止，我可以通过在网站上阅读类似的问题来获得所需的结果，但是我的解决方案似乎并不太"pythonic"，也没有找到一种非常简单的方法.我敢肯定，有一种更有效的方式来绘制表格，所以我问这个问题以更多地了解Python，并让其他人对同一问题有一个直接的答案.在这里:
I am trying to plot a csv formatted table using Python. So far, I was able to get the result I wanted by reading similar questions on the site, but my solution doesn't seem too "pythonic", nor did I found a very straightforward way of doing this. I am sure there is a more efficient way for plotting a table, so I'm asking this question to learn more about Python and let others have a straight answer for the same problem. Here it goes:
我有一个带有数据的表，该表具有标题和第一列.就我而言，分别是几个月和几年.即:
I have a table with data, which have headers and a first column. In my case, it is months and years respectively. i.e.:
年，1月，2月，3月，4月，5月，6月，7月，8月，9月，10月，11月，DIC1998，，0.78,0.60,0.50,0.50 ,,,, 0.62，，0.451999,0.40,0.30,0.28,0.22,0.26,0.50,0.52,0.76,0.89,0.85,0.74,0.672000,0.58,0.58,0.51,0.47,0.63,0.92,1.00,1.00,0.99,1.00,0.96,0.912001,0.86,0.83,0.80,0.71,0.83,0.98,1.05,1.11,1.09,0.99,0.87,0.80...
Year,JAN,FEB,MAR,APR,MAY,JUN,JUL,AUG,SEP,OCT,NOV,DIC 1998,,0.78,0.60,0.50,0.50,,,,,0.62,,0.45 1999,0.40,0.30,0.28,0.22,0.26,0.50,0.52,0.76,0.89,0.85,0.74,0.67 2000,0.58,0.58,0.51,0.47,0.63,0.92,1.00,1.00,0.99,1.00,0.96,0.91 2001,0.86,0.83,0.80,0.71,0.83,0.98,1.05,1.11,1.09,0.99,0.87,0.80 ...
如您所见，也缺少数据.
As you can see, there is missing data too.
我的解决方法如下:
import numpy as np
from matplotlib import pyplot as plt
#Import Data
Data=np.genfromtxt('LakeLevels.csv',delimiter=',',names=True,dtype=float)
#Extract data
Months=list(Data.dtype.names[1:])
Years=Data['Year']
Level=Data.view(dtype=float).reshape(Data.shape + (-1,))[:,1:]
Level_masked= np.ma.array (Level, mask=np.isnan(Level))
#Plot
fig=plt.pcolor(np.linspace(1,12,12),Years,Level_masked)
plt.colorbar()
plt.xticks(range(12),Months,rotation=45)
我发现解决方案对于一个非常简单的任务来说太复杂了.有没有更好的方法来达到相同的结果或我可以改进的部分代码?甚至是自动执行此功能的功能.
I found the solution was too complex for a very simple task. Is there a better way of achieving the same result or parts of the code I can improve? Maybe even a function that does this automatically.
谢谢.
推荐答案
我将根据@Ami Tavory的回答发布最终解决方案.
I will post my final solution based on @Ami Tavory's answer.
import pandas as pd
import seaborn as sns
df = pd.read_csv('LakeLevels.csv', delimiter=',', index_col='Year')
sns.heatmap(df)
因此，通过使用这2个程序包(即pandas和seaborn)，我能够在2行中获得所需的结果！
So by using these 2 packages (i.e. pandas and seaborn) I was able to get my desired result in 2 lines!
最诚挚的问候.
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