Why are ticks not working for date format?
I have this simple python code that generates a simple X date y decimal plot.
My goal was to set the ticks to be the one unit less than the minimum TO the one unit plus the maximum, see code and plot below.
I don't see why it would not shows the X tick properly, the Y tick shows just fine.
import matplotlib.pyplot as plt
import pandas as pd
fig = plt.figure(facecolor="#979899")
ax = plt.gca()
ax.set_facecolor("#d1d1d1")
ax.set_xlim(pd.to_datetime(["2018-11-18"]),pd.to_datetime(["2018-11-22"]))
plt.grid(True)
plt.title("This is a title",fontsize=16)
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])
plt.xticks(pd.to_datetime(["2018-11-18","2018-11-20","2018-11-22"]))
x1 = pd.to_datetime(["2018-11-19","2018-11-20","2018-11-21"])
y1 = [0.18,0.32,0.21]
for i,item in enumerate(y1):
xP = x1[i]
yP = y1[i]
plt.text(xP,yP,str(item)+"%",fontsize=11)
plt.scatter(x1,y1)
plt.plot(x1,y1)
plt.show()
python matplotlib
add a comment |
I have this simple python code that generates a simple X date y decimal plot.
My goal was to set the ticks to be the one unit less than the minimum TO the one unit plus the maximum, see code and plot below.
I don't see why it would not shows the X tick properly, the Y tick shows just fine.
import matplotlib.pyplot as plt
import pandas as pd
fig = plt.figure(facecolor="#979899")
ax = plt.gca()
ax.set_facecolor("#d1d1d1")
ax.set_xlim(pd.to_datetime(["2018-11-18"]),pd.to_datetime(["2018-11-22"]))
plt.grid(True)
plt.title("This is a title",fontsize=16)
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])
plt.xticks(pd.to_datetime(["2018-11-18","2018-11-20","2018-11-22"]))
x1 = pd.to_datetime(["2018-11-19","2018-11-20","2018-11-21"])
y1 = [0.18,0.32,0.21]
for i,item in enumerate(y1):
xP = x1[i]
yP = y1[i]
plt.text(xP,yP,str(item)+"%",fontsize=11)
plt.scatter(x1,y1)
plt.plot(x1,y1)
plt.show()
python matplotlib
2
The answer to the "why" of it is:matplotlib
simply does not know that you intend the x-axis numbers to be treated as datestamps. By default Python, and hencematplotlib
, assumes that your 8-digit literals are decimal numbers in the neighborhood of twenty million. If your question is really "how do I plot with datestamps on the x axis?" then a google search formatplotlib date tick labels
can get you to the following example code from the matplotlib project's own gallery: matplotlib.org/gallery/api/date.html
– jez
Nov 22 '18 at 23:38
oh I totally see that you are saying
– RollRoll
Nov 22 '18 at 23:39
Why do you want to show ticks outside your data range?
– Joooeey
Nov 22 '18 at 23:57
If you just want a quick and dirty graph, you could use strings on the x-axisx1 = ['20181120', '20181121', '20181122']
. But that will only work if the dates are increasing and all one day apart.
– Joooeey
Nov 22 '18 at 23:59
add a comment |
I have this simple python code that generates a simple X date y decimal plot.
My goal was to set the ticks to be the one unit less than the minimum TO the one unit plus the maximum, see code and plot below.
I don't see why it would not shows the X tick properly, the Y tick shows just fine.
import matplotlib.pyplot as plt
import pandas as pd
fig = plt.figure(facecolor="#979899")
ax = plt.gca()
ax.set_facecolor("#d1d1d1")
ax.set_xlim(pd.to_datetime(["2018-11-18"]),pd.to_datetime(["2018-11-22"]))
plt.grid(True)
plt.title("This is a title",fontsize=16)
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])
plt.xticks(pd.to_datetime(["2018-11-18","2018-11-20","2018-11-22"]))
x1 = pd.to_datetime(["2018-11-19","2018-11-20","2018-11-21"])
y1 = [0.18,0.32,0.21]
for i,item in enumerate(y1):
xP = x1[i]
yP = y1[i]
plt.text(xP,yP,str(item)+"%",fontsize=11)
plt.scatter(x1,y1)
plt.plot(x1,y1)
plt.show()
python matplotlib
I have this simple python code that generates a simple X date y decimal plot.
My goal was to set the ticks to be the one unit less than the minimum TO the one unit plus the maximum, see code and plot below.
I don't see why it would not shows the X tick properly, the Y tick shows just fine.
import matplotlib.pyplot as plt
import pandas as pd
fig = plt.figure(facecolor="#979899")
ax = plt.gca()
ax.set_facecolor("#d1d1d1")
ax.set_xlim(pd.to_datetime(["2018-11-18"]),pd.to_datetime(["2018-11-22"]))
plt.grid(True)
plt.title("This is a title",fontsize=16)
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])
plt.xticks(pd.to_datetime(["2018-11-18","2018-11-20","2018-11-22"]))
x1 = pd.to_datetime(["2018-11-19","2018-11-20","2018-11-21"])
y1 = [0.18,0.32,0.21]
for i,item in enumerate(y1):
xP = x1[i]
yP = y1[i]
plt.text(xP,yP,str(item)+"%",fontsize=11)
plt.scatter(x1,y1)
plt.plot(x1,y1)
plt.show()
python matplotlib
python matplotlib
edited Nov 24 '18 at 1:53
RollRoll
asked Nov 22 '18 at 23:24
RollRollRollRoll
3,4211354113
3,4211354113
2
The answer to the "why" of it is:matplotlib
simply does not know that you intend the x-axis numbers to be treated as datestamps. By default Python, and hencematplotlib
, assumes that your 8-digit literals are decimal numbers in the neighborhood of twenty million. If your question is really "how do I plot with datestamps on the x axis?" then a google search formatplotlib date tick labels
can get you to the following example code from the matplotlib project's own gallery: matplotlib.org/gallery/api/date.html
– jez
Nov 22 '18 at 23:38
oh I totally see that you are saying
– RollRoll
Nov 22 '18 at 23:39
Why do you want to show ticks outside your data range?
– Joooeey
Nov 22 '18 at 23:57
If you just want a quick and dirty graph, you could use strings on the x-axisx1 = ['20181120', '20181121', '20181122']
. But that will only work if the dates are increasing and all one day apart.
– Joooeey
Nov 22 '18 at 23:59
add a comment |
2
The answer to the "why" of it is:matplotlib
simply does not know that you intend the x-axis numbers to be treated as datestamps. By default Python, and hencematplotlib
, assumes that your 8-digit literals are decimal numbers in the neighborhood of twenty million. If your question is really "how do I plot with datestamps on the x axis?" then a google search formatplotlib date tick labels
can get you to the following example code from the matplotlib project's own gallery: matplotlib.org/gallery/api/date.html
– jez
Nov 22 '18 at 23:38
oh I totally see that you are saying
– RollRoll
Nov 22 '18 at 23:39
Why do you want to show ticks outside your data range?
– Joooeey
Nov 22 '18 at 23:57
If you just want a quick and dirty graph, you could use strings on the x-axisx1 = ['20181120', '20181121', '20181122']
. But that will only work if the dates are increasing and all one day apart.
– Joooeey
Nov 22 '18 at 23:59
2
2
The answer to the "why" of it is:
matplotlib
simply does not know that you intend the x-axis numbers to be treated as datestamps. By default Python, and hence matplotlib
, assumes that your 8-digit literals are decimal numbers in the neighborhood of twenty million. If your question is really "how do I plot with datestamps on the x axis?" then a google search for matplotlib date tick labels
can get you to the following example code from the matplotlib project's own gallery: matplotlib.org/gallery/api/date.html– jez
Nov 22 '18 at 23:38
The answer to the "why" of it is:
matplotlib
simply does not know that you intend the x-axis numbers to be treated as datestamps. By default Python, and hence matplotlib
, assumes that your 8-digit literals are decimal numbers in the neighborhood of twenty million. If your question is really "how do I plot with datestamps on the x axis?" then a google search for matplotlib date tick labels
can get you to the following example code from the matplotlib project's own gallery: matplotlib.org/gallery/api/date.html– jez
Nov 22 '18 at 23:38
oh I totally see that you are saying
– RollRoll
Nov 22 '18 at 23:39
oh I totally see that you are saying
– RollRoll
Nov 22 '18 at 23:39
Why do you want to show ticks outside your data range?
– Joooeey
Nov 22 '18 at 23:57
Why do you want to show ticks outside your data range?
– Joooeey
Nov 22 '18 at 23:57
If you just want a quick and dirty graph, you could use strings on the x-axis
x1 = ['20181120', '20181121', '20181122']
. But that will only work if the dates are increasing and all one day apart.– Joooeey
Nov 22 '18 at 23:59
If you just want a quick and dirty graph, you could use strings on the x-axis
x1 = ['20181120', '20181121', '20181122']
. But that will only work if the dates are increasing and all one day apart.– Joooeey
Nov 22 '18 at 23:59
add a comment |
2 Answers
2
active
oldest
votes
You provide integers for the dates. So Matplotlib treats it like any number. And it tries to be smart about large numbers that are close together:
The default formatter identifies when the x-data being plotted is a
small range on top of a large off set. To reduce the chances that the
ticklabels overlap the ticks are labeled as deltas from a fixed
offset. For example:
ax.plot(np.arange(2000, 2010), range(10))
will have tick of0-9
with
an offset of+2e3
. If this is not desired turn off the use of the
offset on the default formatter:
ax.get_xaxis().get_major_formatter().set_useOffset(False)
set the
rcParamaxes.formatter.useoffset=False
to turn it off globally, or set
a different formatter.
This is from the docs of thematplotlib.ticker
module.
add a comment |
I solved the issue by converting to datetime object. Find solution below:
import matplotlib.pyplot as plt
import datetime
fig = plt.figure(facecolor="#979899")
ax = plt.gca()
ax.set_facecolor("#d1d1d1")
plt.grid(True)
plt.title("This is a title",fontsize=16)
plt.xticks([datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)],["11/20","11/21","11/22"])
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],["0.0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1.0"])
x1 = [datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)]
y1 = [0.18,0.32,0.21]
for i,item in enumerate(y1):
xP = x1[i]
yP = y1[i]
plt.text(xP,yP,str(item)+"%",fontsize=11)
plt.plot(x1,y1)
plt.show()
add a comment |
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2 Answers
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active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
You provide integers for the dates. So Matplotlib treats it like any number. And it tries to be smart about large numbers that are close together:
The default formatter identifies when the x-data being plotted is a
small range on top of a large off set. To reduce the chances that the
ticklabels overlap the ticks are labeled as deltas from a fixed
offset. For example:
ax.plot(np.arange(2000, 2010), range(10))
will have tick of0-9
with
an offset of+2e3
. If this is not desired turn off the use of the
offset on the default formatter:
ax.get_xaxis().get_major_formatter().set_useOffset(False)
set the
rcParamaxes.formatter.useoffset=False
to turn it off globally, or set
a different formatter.
This is from the docs of thematplotlib.ticker
module.
add a comment |
You provide integers for the dates. So Matplotlib treats it like any number. And it tries to be smart about large numbers that are close together:
The default formatter identifies when the x-data being plotted is a
small range on top of a large off set. To reduce the chances that the
ticklabels overlap the ticks are labeled as deltas from a fixed
offset. For example:
ax.plot(np.arange(2000, 2010), range(10))
will have tick of0-9
with
an offset of+2e3
. If this is not desired turn off the use of the
offset on the default formatter:
ax.get_xaxis().get_major_formatter().set_useOffset(False)
set the
rcParamaxes.formatter.useoffset=False
to turn it off globally, or set
a different formatter.
This is from the docs of thematplotlib.ticker
module.
add a comment |
You provide integers for the dates. So Matplotlib treats it like any number. And it tries to be smart about large numbers that are close together:
The default formatter identifies when the x-data being plotted is a
small range on top of a large off set. To reduce the chances that the
ticklabels overlap the ticks are labeled as deltas from a fixed
offset. For example:
ax.plot(np.arange(2000, 2010), range(10))
will have tick of0-9
with
an offset of+2e3
. If this is not desired turn off the use of the
offset on the default formatter:
ax.get_xaxis().get_major_formatter().set_useOffset(False)
set the
rcParamaxes.formatter.useoffset=False
to turn it off globally, or set
a different formatter.
This is from the docs of thematplotlib.ticker
module.
You provide integers for the dates. So Matplotlib treats it like any number. And it tries to be smart about large numbers that are close together:
The default formatter identifies when the x-data being plotted is a
small range on top of a large off set. To reduce the chances that the
ticklabels overlap the ticks are labeled as deltas from a fixed
offset. For example:
ax.plot(np.arange(2000, 2010), range(10))
will have tick of0-9
with
an offset of+2e3
. If this is not desired turn off the use of the
offset on the default formatter:
ax.get_xaxis().get_major_formatter().set_useOffset(False)
set the
rcParamaxes.formatter.useoffset=False
to turn it off globally, or set
a different formatter.
This is from the docs of thematplotlib.ticker
module.
answered Nov 23 '18 at 0:04
JoooeeyJoooeey
632818
632818
add a comment |
add a comment |
I solved the issue by converting to datetime object. Find solution below:
import matplotlib.pyplot as plt
import datetime
fig = plt.figure(facecolor="#979899")
ax = plt.gca()
ax.set_facecolor("#d1d1d1")
plt.grid(True)
plt.title("This is a title",fontsize=16)
plt.xticks([datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)],["11/20","11/21","11/22"])
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],["0.0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1.0"])
x1 = [datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)]
y1 = [0.18,0.32,0.21]
for i,item in enumerate(y1):
xP = x1[i]
yP = y1[i]
plt.text(xP,yP,str(item)+"%",fontsize=11)
plt.plot(x1,y1)
plt.show()
add a comment |
I solved the issue by converting to datetime object. Find solution below:
import matplotlib.pyplot as plt
import datetime
fig = plt.figure(facecolor="#979899")
ax = plt.gca()
ax.set_facecolor("#d1d1d1")
plt.grid(True)
plt.title("This is a title",fontsize=16)
plt.xticks([datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)],["11/20","11/21","11/22"])
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],["0.0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1.0"])
x1 = [datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)]
y1 = [0.18,0.32,0.21]
for i,item in enumerate(y1):
xP = x1[i]
yP = y1[i]
plt.text(xP,yP,str(item)+"%",fontsize=11)
plt.plot(x1,y1)
plt.show()
add a comment |
I solved the issue by converting to datetime object. Find solution below:
import matplotlib.pyplot as plt
import datetime
fig = plt.figure(facecolor="#979899")
ax = plt.gca()
ax.set_facecolor("#d1d1d1")
plt.grid(True)
plt.title("This is a title",fontsize=16)
plt.xticks([datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)],["11/20","11/21","11/22"])
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],["0.0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1.0"])
x1 = [datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)]
y1 = [0.18,0.32,0.21]
for i,item in enumerate(y1):
xP = x1[i]
yP = y1[i]
plt.text(xP,yP,str(item)+"%",fontsize=11)
plt.plot(x1,y1)
plt.show()
I solved the issue by converting to datetime object. Find solution below:
import matplotlib.pyplot as plt
import datetime
fig = plt.figure(facecolor="#979899")
ax = plt.gca()
ax.set_facecolor("#d1d1d1")
plt.grid(True)
plt.title("This is a title",fontsize=16)
plt.xticks([datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)],["11/20","11/21","11/22"])
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],["0.0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1.0"])
x1 = [datetime.date(2018,11,20),datetime.date(2018,11,21),datetime.date(2018,11,22)]
y1 = [0.18,0.32,0.21]
for i,item in enumerate(y1):
xP = x1[i]
yP = y1[i]
plt.text(xP,yP,str(item)+"%",fontsize=11)
plt.plot(x1,y1)
plt.show()
answered Nov 24 '18 at 3:02
RollRollRollRoll
3,4211354113
3,4211354113
add a comment |
add a comment |
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2
The answer to the "why" of it is:
matplotlib
simply does not know that you intend the x-axis numbers to be treated as datestamps. By default Python, and hencematplotlib
, assumes that your 8-digit literals are decimal numbers in the neighborhood of twenty million. If your question is really "how do I plot with datestamps on the x axis?" then a google search formatplotlib date tick labels
can get you to the following example code from the matplotlib project's own gallery: matplotlib.org/gallery/api/date.html– jez
Nov 22 '18 at 23:38
oh I totally see that you are saying
– RollRoll
Nov 22 '18 at 23:39
Why do you want to show ticks outside your data range?
– Joooeey
Nov 22 '18 at 23:57
If you just want a quick and dirty graph, you could use strings on the x-axis
x1 = ['20181120', '20181121', '20181122']
. But that will only work if the dates are increasing and all one day apart.– Joooeey
Nov 22 '18 at 23:59