I have pulled in a dataset that I want to use, with columns named Date and Adjusted. Adjusted is just the adjusted percentage growth on the base month.
The code I currently have is:
x = data['Date']
y = data['Adjusted']
fig = plt.figure(dpi=128, figsize=(7,3))
plt.plot(x,y)
plt.title("FTSE 100 Growth", fontsize=25)
plt.xlabel("Date", fontsize=14)
plt.ylabel("Adjusted %", fontsize=14)
plt.show()
However, when I run it I get essentially a solid black line across the bottom where all of the dates are covering each other up. It is trying to show every single date, when obviously I only want to show major ones. That dates are in the format Apr-19, and the data runs from Oct-03 to May-20.
How do I limit the number of date ticks and labels to one per year, or any amount I choose? If you do have a solution, if you could respond with the edits made to the code itself that would be great. I've tried other solutions I've found on here but I haven't been able to get it to work.
dates module of matplotlib will do the job. You can control the interval by modifying the MonthLocator (It's currently set to 6 months). Here's how:
import pandas as pd
from datetime import date, datetime, timedelta
import matplotlib.pyplot as plt
import matplotlib.dates as md
import numpy as np
import matplotlib.ticker as ticker
x = data['Date']
y = data['Adjusted']
#converts differently formatted date to a datetime object
def convert_date(df):
return datetime.strptime(df['Date'], '%b-%y')
data['Formatted_Date'] = data.apply(convert_date, axis=1)
# plot
fig, ax = plt.subplots(1, 1)
ax.plot(data['Formatted_Date'], y,'ok')
## Set time format and the interval of ticks (every 6 months)
xformatter = md.DateFormatter('%Y-%m') # format as year, month
xlocator = md.MonthLocator(interval = 6)
## Set xtick labels to appear every 6 months
ax.xaxis.set_major_locator(xlocator)
## Format xtick labels as YYYY:mm
plt.gcf().axes[0].xaxis.set_major_formatter(xformatter)
plt.title("FTSE 100 Growth", fontsize=25)
plt.xlabel("Date", fontsize=14)
plt.ylabel("Adjusted %", fontsize=14)
plt.show()
Example output:
Related
I am trying to represent CDC Delay of Care data as a line graph but am having some trouble formatting the y axis so that it is a percentage to the hundredths place. I would also like for the x axis to show every year in the range selected.
Here is my code:
import pandas as pd
from isolation import isolate_total_stub, isolate_age_stub
import matplotlib.pyplot as plt
# very simple extraction, drop some columns and check some data
cdc_data = pd.read_csv('CDC_Delay_of_Care_Data.csv')
# separate the categories of delayed care
delay_of_medical_care = cdc_data[cdc_data.PANEL == 'Delay or nonreceipt of needed medical care due to cost']
# isolate the totals stub
total_delay_of_medical_care = isolate_total_stub(delay_of_medical_care)
x_axis = total_delay_of_medical_care.YEAR
y_axis = total_delay_of_medical_care.ESTIMATE
plt.plot(x_axis, y_axis)
plt.xlabel('Year')
plt.ylabel('Percentage')
plt.show()
The graph that displays looks like this:
line graph
Excuse me for being a novice, I have been googling for an hour now and instead of continue to search for an answer I thought it would be more productive to ask StackOverflow.
Thank you for your time.
To change the format of Y-axis, you can use set_major_formatter
To change X-axis to date in year format, you will need to use set_major_locator, assuming that your date is in datetime format
To change format of X-axis, you can again use the set_major_formatter
I am showing a small example below with dummy data. Hope this works.
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import matplotlib.dates as mdate
estimate = [8, 7.1, 11, 10.6, 8, 8.3]
year = ['2000-01-01', '2004-01-01', '2008-01-01', '2012-01-01', '2016-01-01', '2020-01-01']
year=pd.to_datetime(year) ## Convert string to datetime
plt.figure(figsize=(12,5)) ## Added so the Years don't overlap on each other
plt.plot(year, estimate)
plt.xlabel('Year')
plt.ylabel('Percentage')
plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%.2f')) ## Makes X-axis label with two decimal points
locator = mdate.YearLocator()
plt.gca().xaxis.set_major_locator(locator) ## Changes datetime to years - 1 label per year
plt.gca().xaxis.set_major_formatter(mdate.DateFormatter('%Y')) ## Shows X-axis in Years
plt.gcf().autofmt_xdate() ## Rotates X-labels, if you want to use it
plt.show()
Output plot
I am trying to create a plot with an amount (int) in the y-axis and days in the x-axis.
I want the plot to always have the whole month in the x-axis although I dont have data for all days.
This is the code I tryed:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
import datetime as dt
df=get_pandas_data(datab) #Taking data from database in pandas DataFrame
fig = plt.figure(figsize=(10,10)) #Initialize plot
ax1 = fig.add_subplot(1,1,1)
dates=[dt.datetime.strptime(d,'%Y-%m-%d').date() for d in df['date']]
dates=list(set(dates)) #Takes all the dates from de Dataframe and sets to avoid repeated dates
s=df.resample('D', on='date')['amount'].sum() #Takes the total amount for the same date
ax1.bar(dates,s) #Bar plot for dates and amount
ax1.set(xlabel="Date",
ylabel="Balance (€)",
title="Total Monthly balance") # Plot information
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
#this is soposed to set all days of the month in the x-axis
ax1.xaxis.set_major_locator(mdates.DayLocator(interval=1))
fig.autofmt_xdate()
plt.show()
The result I get from this is a plot but only with those days that have data.
How can I make the plot to have all days in the month and plot the bar on those who have data?
This works fine with bare datetimes and matplotlib so you must be malforming your data somehow when doing your pandas manipulations. But we can't really help because we don't have your dataframe. Its always preferable to create a standalone example with dummy data, and as little code as possible to recreate the issue. a) 90% of the time you will realize your problem b) if not, we can help...
import numpy as np
import matplotlib.pyplot as plt
import datetime
x = np.array([1, 3, 7, 8, 10])
y = x * 2
dates = [datetime.datetime(2000, 2, xx) for xx in x]
fig, ax = plt.subplots()
ax.bar(dates, y)
fig.autofmt_xdate()
plt.show()
Let's say I have one-minute data during business hours of 8am to 4pm over three days. I would like to plot these data using the pandas plot function:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(51723)
dates = pd.date_range("11/8/2018", "11/11/2018", freq = "min")
df = pd.DataFrame(np.random.rand(len(dates)), index = dates, columns = ['A'])
df = df[(df.index.hour >= 8) & (df.index.hour <= 16)] # filter for business hours
fig, ax = plt.subplots()
df.plot(ax = ax)
plt.show()
However, the plot function also includes overnight hours in the plot, resulting in unintended plotting during this time:
I would the data to be plotted contiguously, ignoring the overnight time (something like this):
What is a good way to plot only the intended hours of 8am to 4pm?
This can be done by plotting each date on a different axis. But things like the labels will get cramped in certain cases.
import datetime
import matplotlib.pyplot as plt
pdates = np.unique(df.index.date) # Unique Dates
fig, ax = plt.subplots(ncols=len(pdates), sharey=True, figsize=(18,6))
# Adjust spacing between suplots
# (Set to 0 for continuous, though labels will overlap)
plt.subplots_adjust(wspace=0.05)
# Plot all data on each subplot, adjust the limits of each accordingly
for i in range(len(pdates)):
df.plot(ax=ax[i], legend=None)
# Hours 8-16 each day:
ax[i].set_xlim(datetime.datetime.combine(pdates[i], datetime.time(8)),
datetime.datetime.combine(pdates[i], datetime.time(16)))
# Deal with spines for each panel
if i !=0:
ax[i].spines['left'].set_visible(False)
ax[i].tick_params(right=False,
which='both',
left=False,
axis='y')
if i != len(pdates)-1:
ax[i].spines['right'].set_visible(False)
plt.show()
I am trying to do analysis on a bike share dataset. Part of the analysis includes showing the weekends' demand in date wise plot.
My dataframe in pandas with last 5 row looks like this.
Here is my code for date vs total ride plot.
import seaborn as sns
sns.set_style("darkgrid")
plt.plot(d17_day_count)
plt.show()
.
I want to highlight weekends in the plot. So that it could look something similar to this plot.
I am using Python with matplotlib and seaborn library.
You can easily highlight areas by using axvspan, to get the areas to be highlighted you can run through the index of your dataframe and search for the weekend days. I've also added an example for highlighting 'occupied hours' during a working week (hopefully that doesn't confuse things).
I've created dummy data for a dataframe based on days and another one for hours.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# dummy data (Days)
dates_d = pd.date_range('2017-01-01', '2017-02-01', freq='D')
df = pd.DataFrame(np.random.randint(1, 20, (dates_d.shape[0], 1)))
df.index = dates_d
# dummy data (Hours)
dates_h = pd.date_range('2017-01-01', '2017-02-01', freq='H')
df_h = pd.DataFrame(np.random.randint(1, 20, (dates_h.shape[0], 1)))
df_h.index = dates_h
#two graphs
fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True)
#plot lines
dfs = [df, df_h]
for i, df in enumerate(dfs):
for v in df.columns.tolist():
axes[i].plot(df[v], label=v, color='black', alpha=.5)
def find_weekend_indices(datetime_array):
indices = []
for i in range(len(datetime_array)):
if datetime_array[i].weekday() >= 5:
indices.append(i)
return indices
def find_occupied_hours(datetime_array):
indices = []
for i in range(len(datetime_array)):
if datetime_array[i].weekday() < 5:
if datetime_array[i].hour >= 7 and datetime_array[i].hour <= 19:
indices.append(i)
return indices
def highlight_datetimes(indices, ax):
i = 0
while i < len(indices)-1:
ax.axvspan(df.index[indices[i]], df.index[indices[i] + 1], facecolor='green', edgecolor='none', alpha=.5)
i += 1
#find to be highlighted areas, see functions
weekend_indices = find_weekend_indices(df.index)
occupied_indices = find_occupied_hours(df_h.index)
#highlight areas
highlight_datetimes(weekend_indices, axes[0])
highlight_datetimes(occupied_indices, axes[1])
#formatting..
axes[0].xaxis.grid(b=True, which='major', color='black', linestyle='--', alpha=1) #add xaxis gridlines
axes[1].xaxis.grid(b=True, which='major', color='black', linestyle='--', alpha=1) #add xaxis gridlines
axes[0].set_xlim(min(dates_d), max(dates_d))
axes[0].set_title('Weekend days', fontsize=10)
axes[1].set_title('Occupied hours', fontsize=10)
plt.show()
I tried using the code in the accepted answer but the way the indices are used, the last weekend in the time series does not get highlighted entirely, despite what the image currently shown suggests (this is noticeable mainly with a frequency of 6 hours or more). Also, it does not work if the frequency of the data is higher than daily. This is why I share here a solution that uses the x-axis units so that weekends (or any other recurring time period) can be highlighted without any problem related to the index.
This solution takes only 6 lines of code and it works with any frequency. In the example below, it highlights full weekend days which makes it more efficient than the accepted answer where small frequencies (e.g. 30 minutes) will produce many polygons to cover the whole weekend.
The x-axis limits are used to compute the range of time covered by the plot in terms of days, which is the unit used for matplotlib dates. Then a weekends mask is computed and passed to the where argument of the fill_between plotting function. The masks are processed as right-exclusive so in this case, they must contain Mondays for the highlights to be drawn up to Mondays 00:00. Because plotting these highlights can alter the x-axis limits when weekends occur near the limits, the x-axis limits are set back to the original values after plotting.
Note that contrary to axvspan, the fill_between function needs the y1 and y2 arguments. For some reason, using the default y-axis limits leaves a small gap between the plot frame and the tops and bottoms of the weekend highlights. This issue is solved by running ax.set_ylim(*ax.get_ylim()) just after creating the plot.
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
import matplotlib.pyplot as plt # v 3.3.2
import matplotlib.dates as mdates
# Create sample dataset
rng = np.random.default_rng(seed=1234) # random number generator
dti = pd.date_range('2017-01-01', '2017-05-15', freq='D')
counts = 5000 + np.cumsum(rng.integers(-1000, 1000, size=dti.size))
df = pd.DataFrame(dict(Counts=counts), index=dti)
# Draw pandas plot: x_compat=True converts the pandas x-axis units to matplotlib
# date units (not strictly necessary when using a daily frequency like here)
ax = df.plot(x_compat=True, figsize=(10, 5), legend=None, ylabel='Counts')
ax.set_ylim(*ax.get_ylim()) # reset y limits to display highlights without gaps
# Highlight weekends based on the x-axis units
xmin, xmax = ax.get_xlim()
days = np.arange(np.floor(xmin), np.ceil(xmax)+2)
weekends = [(dt.weekday()>=5)|(dt.weekday()==0) for dt in mdates.num2date(days)]
ax.fill_between(days, *ax.get_ylim(), where=weekends, facecolor='k', alpha=.1)
ax.set_xlim(xmin, xmax) # set limits back to default values
# Create appropriate ticks using matplotlib date tick locators and formatters
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.MonthLocator(bymonthday=np.arange(5, 31, step=7)))
ax.xaxis.set_major_formatter(mdates.DateFormatter('\n%b'))
ax.xaxis.set_minor_formatter(mdates.DateFormatter('%d'))
# Additional formatting
ax.figure.autofmt_xdate(rotation=0, ha='center')
title = 'Daily count of trips with weekends highlighted from SAT 00:00 to MON 00:00'
ax.set_title(title, pad=20, fontsize=14);
As you can see, the weekends are always highlighted to the full extent, regardless of where the data starts and ends.
You can find more examples of this solution in the answers I have posted here and here.
I have another suggestion to make in this regard, which takes inspirations from previous posts by other contributors. The code is as follows:
import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rng = np.random.default_rng(seed=42) # random number generator
dti = pd.date_range('2021-08-01', '2021-08-31', freq='D')
counts = 5000 + np.cumsum(rng.integers(-1000, 1000, size=dti.size))
df = pd.DataFrame(dict(Counts=counts), index=dti)
weekends = [d for d in df.index if d.isoweekday() in [6,7]]
weekend_list = []
for weekendday in weekends:
d1 = weekendday
d2 = weekendday + datetime.timedelta(days=1)
weekend_list.append((d1, d2))
weekend_df = pd.DataFrame(weekend_list)
sns.set()
plt.figure(figsize=(15, 10), dpi=100)
df.plot()
plt.legend(bbox_to_anchor=(1.02, 0), loc="lower left", borderaxespad=0)
plt.ylabel("Counts")
plt.xlabel("Date of visit")
plt.xticks(rotation = 0)
plt.title("Daily counts of shop visits with weekends highlighted in green")
ax = plt.gca()
for d in weekend_df.index:
print(weekend_df[0][d], weekend_df[1][d])
ax.axvspan(weekend_df[0][d], weekend_df[1][d], facecolor="g", edgecolor="none", alpha=0.5)
ax.relim()
ax.autoscale_view()
plt.savefig("junk.png", dpi=100, bbox_inches='tight', pad_inches=0.2)
The result would be something like the following diagram:
I have data that shows some values collected on three different dates: 2015-01-08, 2015-01-09 and 2015-01-12. For each date there are several data points that have timestamps.
Date/times are in a list and it looks as follows:
['2015-01-08-09:00:00', '2015-01-08-10:00:00', '2015-01-08-11:00:00', '2015-01-08-12:00:00', '2015-01-08-13:00:00', '2015-01-09-14:00:00', '2015-01-09-15:00:00', '2015-01-09-16:00:00', '2015-01-12-09:00:00', '2015-01-12-10:00:00', '2015-01-12-11:00:00']
On the other hand I have corresponding values (floats) in another list:
[12210.0, 12210.0, 12180.0, 12240.0, 12250.0, 12420.0, 12390.0, 12400.0, 12380.0, 12450.0, 12460.0]
To put all this together and plot a graph I use following code:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.dates as md
import dateutil
from matplotlib.font_manager import FontProperties
timestamps = ['2015-01-08-09:00:00', '2015-01-08-10:00:00', '2015-01-08-11:00:00', '2015-01-08-12:00:00', '2015-01-08-13:00:00', '2015-01-09-14:00:00', '2015-01-09-15:00:00', '2015-01-09-16:00:00', '2015-01-12-09:00:00', '2015-01-12-10:00:00', '2015-01-12-11:00:00']
ticks = [12210.0, 12210.0, 12180.0, 12240.0, 12250.0, 12420.0, 12390.0, 12400.0, 12380.0, 12450.0, 12460.0]
plt.subplots_adjust(bottom=0.2)
plt.xticks( rotation=90 )
dates = [dateutil.parser.parse(s) for s in timestamps]
ax=plt.gca()
ax.set_xticks(dates)
ax.tick_params(axis='x', labelsize=8)
xfmt = md.DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(dates, ticks, label="Price")
plt.xlabel("Date and time", fontsize=12)
plt.ylabel("Price", fontsize=12)
plt.suptitle("Price during last three days", fontsize=12)
plt.legend(loc=0,prop={'size':8})
plt.savefig("figure.pdf")
When I try to plot these datetimes and values I get a messy graph with the line going back and forth.
It looks like the dates are being ignored and only timestamps are taken in account which is the reason for the messy chart. I tried to edit the datetimes to have the same date and consecutive timestamps and it fixed the chart. However, I must have dates as well..
What am I doing wrong?
When I try to plot these datetimes and values I get a messy graph with the line going back and forth.
Your plots are going all over the place because plt.plot connects the dots in the order you give it. If this order is not monotonically increasing in x, then it looks "messy". You can sort the points by x first to fix this. Here is a minimal example:
import numpy as np
import pylab as plt
X = np.random.random(20)
Y = 2*X+np.random.random(20)
idx = np.argsort(X)
X2 = X[idx]
Y2 = Y[idx]
fig,ax = plt.subplots(2,1)
ax[0].plot(X,Y)
ax[1].plot(X2,Y2)
plt.show()