import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
corona_data = pd.read_csv("서울시 코로나19 확진자 현황 csv.csv", encoding="cp949")
confirmed_dates = corona_data["확진일"]
confirmed_date = [datetime.strptime(date, "%Y-%m-%d") for date in confirmed_dates]
corona_data["확진일"]= confirmed_date
plt.rc('font', family='Malgun Gothic')
corona_data["확진일"].plot(title="확진일 별 확진자 추이")
plt.show()
This plot show x-axis is just number and y-axis is date but I wanna change x-axis is date and y-axis is number how can I solve it?
If your data is in a dataframe, I recommend using Seaborn to visualize it. It has a great API that allows you to plot elements of your dataframe by referening column names. Here is a toy example:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
# Load data
df = pd.read_csv(...)
# Plot scatter plot
sns.scatter(x='col_1', y='col_2', data=df)
plt.show()
Check out the Seaborn documentation for more
The problem seems to be that your dataframe only contains one dataset which are the dated. You could add a column that contains the row numbers and then select what you want to have on x and y axis by passing the column name to the plot function:
import matplotlib.pyplot as plt
from datetime import datetime
corona_data = pd.read_csv("서울시 코로나19 확진자 현황 csv.csv", encoding="cp949")
confirmed_dates = corona_data["확진일"]
confirmed_date = [datetime.strptime(date, "%Y-%m-%d") for date in confirmed_dates]
corona_data["확진일"]= confirmed_date
# now add the numbers to the dataset
corona_data["numbers"]=[i for i in len(confirmed_dates)]
plt.rc('font', family='Malgun Gothic')
# and tell the plot function that you want "확진일" as x ans "numbers" as y axis
corona_data.plot("확진일","numbers",title="확진일 별 확진자 추이")
plt.show()```
Related
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
x=pd.date_range(end=datetime.today(),periods=150,freq='W').to_pydatetime().tolist()
x_1 = np.random.rand(150)
x_2 = np.random.rand(150)/2
fig = plt.figure(figsize=(10,6),dpi=100)
ax=fig.add_subplot(111)
ax.bar(x,x_1,label='x_1')
ax.bar(x,x_2,label='x_2',bottom=x_1)
plt.legend()
plt.show()
The above code will provide this stacked bar chart.
stacked_chart1
Because the x-axis are specified as dates with 1 week apart, the distance between bars are very large.
I would like to change the chart so that the bars are next to each other with no space like the picture below.
x=np.arange(150)
x_1 = np.random.rand(150)
x_2 = np.random.rand(150)/2
fig = plt.figure(figsize=(10,6),dpi=100)
ax=fig.add_subplot(111)
ax.bar(x,x_1,label='x_1')
ax.bar(x,x_2,label='x_2',bottom=x_1)
plt.legend()
plt.show()
stacked_chart2
Except numbers as x-axis, I would still want to keep the dates in chart 1. I am wondering is there a way to do that? Thanks!!
The reason for the difference is that matplotlib will try to simplify the x-axis when you pass a datetime, because usually you cannot fit every date in the x-ticks. It doesn't try this for int or string types, which is why your second sample looks normal.
However I'm unable to figure out why in this particular example why the spacing is so odd. I looked at this post to no avail.
In any case, there are other plotting modules that tend to handle dates a little more elegantly.
import pandas as pd
from datetime import datetime
import plotly.express as px
import numpy as np
x=pd.date_range(end=datetime.today(),periods=150,freq='W').tolist()
x_1 = np.random.rand(150)
x_2 = np.random.rand(150)/2
df = pd.DataFrame({
'date':x,
'x_1':x_1,
'x_2':x_2}).melt(id_vars='date')
px.bar(df, x='date', y='value',color='variable')
Output
I have two lists containing the sunset and sunrise times and the corresponding dates.
It looks like:
sunrises = ['06:30', '06:28', '06:27', ...]
dates = ['3.21', '3.22', '3.23', ...]
I want to make a plot of the sunrise times as the Y axis and the dates as the X axis.
Simply using
ax.plot(dates, sunrises)
ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(7))
ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(7))
plt.show()
can plot the dates correctly, but the time is wrong:
And actually, the sunrise time isn't supposed to be a straight line.
How do I solve this problem?
You need to transform the datetime in string format to the format that matplotlib can comprehend by using datetime
from matplotlib import pyplot as plt
import matplotlib as mpl
from datetime import datetime
import matplotlib.dates as mdates
sunrises = ['06:30', '06:28', '06:27',]
sunrises_dt = [datetime.strptime(item,'%H:%M') for item in sunrises]
dates = ['3.21', '3.22', '3.23',]
fig,ax = plt.subplots()
ax.plot(dates, sunrises_dt)
ax.yaxis.set_major_formatter(mdates.DateFormatter('%H:%M',))
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(1))
plt.show()
This is because your sunrises are not numerical. I'm assuming you'd want them in a form such that "6:30" means 6.5. Which is calculated below:
import matplotlib.pyplot as plt
sunrises = ['06:30', '06:28', '06:27']
# This converts to decimals
sunrises = [float(x[0:2])+(float(x[-2:])/60) for x in sunrises]
dates = ['3.21', '3.22', '3.23']
plt.plot(sunrises, dates)
plt.xlabel('sunrises')
plt.ylabel('dates')
plt.show()
Note, your dates are being treated as decimals. Is this correct?
I have a large Pandas DataFrame that contains three columns: two different dates and one of measurement (floats). I want to plot a 3d figure (eg. trisurf, plot_surface, etc) where the dates are on the x and y axes and measurement is on the z axis. I tried using the suggestions in this post, but it isn't helpful.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as dates
import datetime
import matplotlib.ticker as ticker
import pandas as pd
df = pd.DataFrame()
df['date1'] = pd.date_range(start='2018-01-05', end='2018-04-15', freq='1D')
df['date2'] = pd.date_range(start='2018-01-19', end='2018-04-29', freq='1D')
df['mydata'] = np.sin(2*np.linspace(-1,1,len(df))) # dummy variable
def format_date(x, pos=None):
return dates.num2date(x).strftime('%Y-%m-%d') #use FuncFormatter to format dates
plt.figure()
ax = Axes3D(fig,rect=[0,0.1,1,1]) #make room for date labels
ax.plot_trisurf(df.date1, df.date2, df.mydata, cmap=cm.coolwarm, linewidth=0.2)
ax.w_xaxis.set_major_locator(ticker.FixedLocator(some_dates)) # I want all the dates on my xaxis
ax.w_xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
ax.w_yaxis.set_major_locator(ticker.FixedLocator(some_dates))
ax.w_yaxis.set_major_formatter(ticker.FuncFormatter(format_date))
for tl in ax.w_xaxis.get_ticklabels(): # re-create what autofmt_xdate but with w_xaxis
tl.set_ha('right')
tl.set_rotation(30)
for tl in ax.w_yaxis.get_ticklabels():
tl.set_ha('right')
#tl.set_rotation(30)
ax.set_xlabel('date1')
ax.set_ylabel('date2')
ax.set_zlabel('mydata')
plt.show()
I keep getting the error RuntimeError: Error in qhull Delaunay triangulation calculation: singular input data (exitcode=2); use python verbose option (-v) to see original qhull error. What am I doing wrong and how do I resolve it?
I have a situation with my data. I like the behaviour of .plot() over a data frame. But sometimes it doesn't work, because the frequency of the time index is not an integer.
But reproducing the plot in matplotlib is OK. Just ugly.
The part that bother me the most is the settings of the x axis. The tick frequency and the limits. Is there any easy way that I can reproduce this behaviour in matplotlib?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Create Data
f = lambda x: np.sin(0.1*x) + 0.1*np.random.randn(1,x.shape[0])
x = np.arange(0,217,0.001)
y = f(x)
# Create DataFrame
data = pd.DataFrame(y.transpose(), columns=['dp'], index=None)
data['t'] = pd.date_range('2021-01-01 14:32:09', periods=len(data['dp']),freq='ms')
data.set_index('t', inplace=True)
# Pandas plot()
data.plot()
# Matplotlib plot (ugly x-axis)
plt.plot(data.index,data['dp'])
EDIT: Basically, what I want to achieve is a similar spacing in the xtics labels, and the tight margin adjust of the values. Legends and axis title, I can do them
Pandas output
Matplotlib output
Thanks
You can use some matplotlib date utilities:
Figure.autofmt_xdate() to unrotate and center the date labels
Axis.set_major_locator() to change the interval to 1 min
Axis.set_major_formatter() to reformat as %H:%M
fig, ax = plt.subplots()
ax.plot(data.index, data['dp'])
import matplotlib.dates as mdates
fig.autofmt_xdate(rotation=0, ha='center')
ax.xaxis.set_major_locator(mdates.MinuteLocator(interval=1))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
# uncomment to remove the first `xtick`
# ax.set_xticks(ax.get_xticks()[1:])
I am trying to do a plot of values over time using seaborn linear model plot but I get the error
TypeError: invalid type promotion
I have read that it is not possible to plot pandas date objects, but that seems really strange given seaborn requires you pass a pandas DataFrame to the plots.
Below is a simple example. Does anyone know how I can get this to work?
import pandas as pd
import seaborn as sns; sns.set(color_codes=True)
import matplotlib.pyplot as plt
date = ['1975-12-03','2008-08-20', '2011-03-16']
value = [1,4,5]
df = pd.DataFrame({'date':date, 'value': value})
df['date'] = pd.to_datetime(df['date'])
g = sns.lmplot(x="date", y="value", data=df, size = 4, aspect = 1.5)
I am trying to do a plot like this one I created in r using ggplot hence why I want to use sns.lmplot
You need to convert your dates to floats, then format the x-axis to reinterpret and format the floats into dates.
Here's how I would do this:
import pandas
import seaborn
from matplotlib import pyplot, dates
%matplotlib inline
date = ['1975-12-03','2008-08-20', '2011-03-16']
value = [1,4,5]
df = pandas.DataFrame({
'date': pandas.to_datetime(date), # pandas dates
'datenum': dates.datestr2num(date), # maptlotlib dates
'value': value
})
#pyplot.FuncFormatter
def fake_dates(x, pos):
""" Custom formater to turn floats into e.g., 2016-05-08"""
return dates.num2date(x).strftime('%Y-%m-%d')
fig, ax = pyplot.subplots()
# just use regplot if you don't need a FacetGrid
seaborn.regplot('datenum', 'value', data=df, ax=ax)
# here's the magic:
ax.xaxis.set_major_formatter(fake_dates)
# legible labels
ax.tick_params(labelrotation=45)
I have found a derived solution from Paul H. for plotting timestamp in seaborn. I had to apply it over my data due to some backend error messages that was returning.
In my solution, I added a matplotlib.ticker FuncFormatter over the ax.xaxis.set_major_formatter. This FuncFormatter wraps the fake_dates function. This way, one doesn't need to insert the #pyplot.FuncFormatter beforehand.
Here is my solution:
import pandas
import seaborn
from matplotlib import pyplot, dates
from matplotlib.ticker import FuncFormatter
date = ['1975-12-03','2008-08-20', '2011-03-16']
value = [1,4,5]
df = pandas.DataFrame({
'date': pandas.to_datetime(date), # pandas dates
'datenum': dates.datestr2num(date), # maptlotlib dates
'value': value
})
def fake_dates(x, pos):
""" Custom formater to turn floats into e.g., 2016-05-08"""
return dates.num2date(x).strftime('%Y-%m-%d')
fig, ax = pyplot.subplots()
# just use regplot if you don't need a FacetGrid
seaborn.regplot('datenum', 'value', data=df, ax=ax)
# here's the magic:
ax.xaxis.set_major_formatter(FuncFormatter(fake_dates))
# legible labels
ax.tick_params(labelrotation=45)
fig.tight_layout()
I hope that works.