set marker to scale with y axis (with limits)
0
I want to scale my marker points with my y-axis values in matplotlib. I thought i could use "counts" from pandas to count the instances of each y-axis value, then set that range to the size values of my scatterplot, but the scale is all messed up and the marker points become huge (the range of y-axis values range from 1 to 17,000). What is a good way to scale this number back to something between 1 and 10?
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_excel("dataset.xlsx")
scale_range = pd.value_counts(data["y_axis"].values, sort = True))
plt.scatter(data["x_axis"], data["y_axis"], s = scale_range)
plt.axis([0, 120, 0, 1])
plt.show('dataset_out.png')
Any help is appreciated, thanks.
python matplotlib
asked Nov 20 '18 at 19:49
Dane KaniaDane Kania
256
|
show 2 more comments
0
I want to scale my marker points with my y-axis values in matplotlib. I thought i could use "counts" from pandas to count the instances of each y-axis value, then set that range to the size values of my scatterplot, but the scale is all messed up and the marker points become huge (the range of y-axis values range from 1 to 17,000). What is a good way to scale this number back to something between 1 and 10?
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_excel("dataset.xlsx")
scale_range = pd.value_counts(data["y_axis"].values, sort = True))
plt.scatter(data["x_axis"], data["y_axis"], s = scale_range)
plt.axis([0, 120, 0, 1])
plt.show('dataset_out.png')
Any help is appreciated, thanks.
python matplotlib
asked Nov 20 '18 at 19:49
Dane KaniaDane Kania
256
2
Divide by 1700?
– busybear
Nov 20 '18 at 19:52
lol yeah i gues i could find some percent to make the values in line to the scale i want. I was looking for a more "pythonic" way of doing it, Thanks though!
– Dane Kania
Nov 20 '18 at 19:54
I think that's a pretty "pythonic" method. It's straightforward at least. Another thing you might want to consider issizesets the area and not the diameter.
– busybear
Nov 20 '18 at 20:03
I wonder why that approach works at all.value_countswill produce a series of unique elements of its argument, which are generally less than the number of total elements. So either each value occurs exactly once, then you get all1inscale_range, orscale_rangedoes not even have as many elements asx_axis, which might throw an error, or at least will give an incorrect plot. In that sense, why not use the y_axis values themselves?plt.scatter(data["x_axis"], data["y_axis"], s = data["y_axis"]+10)?
– ImportanceOfBeingErnest
Nov 20 '18 at 20:06
I tried your method and it doesnt scale at all. Im not really sure why, your method seems like a fine idea.
– Dane Kania
Nov 20 '18 at 20:25
|
show 2 more comments
0
0
0
I want to scale my marker points with my y-axis values in matplotlib. I thought i could use "counts" from pandas to count the instances of each y-axis value, then set that range to the size values of my scatterplot, but the scale is all messed up and the marker points become huge (the range of y-axis values range from 1 to 17,000). What is a good way to scale this number back to something between 1 and 10?
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_excel("dataset.xlsx")
scale_range = pd.value_counts(data["y_axis"].values, sort = True))
plt.scatter(data["x_axis"], data["y_axis"], s = scale_range)
plt.axis([0, 120, 0, 1])
plt.show('dataset_out.png')
Any help is appreciated, thanks.
python matplotlib
asked Nov 20 '18 at 19:49
Dane KaniaDane Kania
256
I want to scale my marker points with my y-axis values in matplotlib. I thought i could use "counts" from pandas to count the instances of each y-axis value, then set that range to the size values of my scatterplot, but the scale is all messed up and the marker points become huge (the range of y-axis values range from 1 to 17,000). What is a good way to scale this number back to something between 1 and 10?
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_excel("dataset.xlsx")
scale_range = pd.value_counts(data["y_axis"].values, sort = True))
plt.scatter(data["x_axis"], data["y_axis"], s = scale_range)
plt.axis([0, 120, 0, 1])
plt.show('dataset_out.png')
Any help is appreciated, thanks.
python matplotlib
python matplotlib
asked Nov 20 '18 at 19:49
Dane KaniaDane Kania
256
asked Nov 20 '18 at 19:49
Dane KaniaDane Kania
256
edited Nov 20 '18 at 19:52
Dane Kania
asked Nov 20 '18 at 19:49
Dane KaniaDane Kania
256
asked Nov 20 '18 at 19:49
Dane KaniaDane Kania
256
asked Nov 20 '18 at 19:49
Dane KaniaDane Kania
256
256
2
Divide by 1700?
– busybear
Nov 20 '18 at 19:52
lol yeah i gues i could find some percent to make the values in line to the scale i want. I was looking for a more "pythonic" way of doing it, Thanks though!
– Dane Kania
Nov 20 '18 at 19:54
I think that's a pretty "pythonic" method. It's straightforward at least. Another thing you might want to consider issizesets the area and not the diameter.
– busybear
Nov 20 '18 at 20:03
I wonder why that approach works at all.value_countswill produce a series of unique elements of its argument, which are generally less than the number of total elements. So either each value occurs exactly once, then you get all1inscale_range, orscale_rangedoes not even have as many elements asx_axis, which might throw an error, or at least will give an incorrect plot. In that sense, why not use the y_axis values themselves?plt.scatter(data["x_axis"], data["y_axis"], s = data["y_axis"]+10)?
– ImportanceOfBeingErnest
Nov 20 '18 at 20:06
I tried your method and it doesnt scale at all. Im not really sure why, your method seems like a fine idea.
– Dane Kania
Nov 20 '18 at 20:25
|
show 2 more comments
2
Divide by 1700?
– busybear
Nov 20 '18 at 19:52
lol yeah i gues i could find some percent to make the values in line to the scale i want. I was looking for a more "pythonic" way of doing it, Thanks though!
– Dane Kania
Nov 20 '18 at 19:54
I think that's a pretty "pythonic" method. It's straightforward at least. Another thing you might want to consider issizesets the area and not the diameter.
– busybear
Nov 20 '18 at 20:03
I wonder why that approach works at all.value_countswill produce a series of unique elements of its argument, which are generally less than the number of total elements. So either each value occurs exactly once, then you get all1inscale_range, orscale_rangedoes not even have as many elements asx_axis, which might throw an error, or at least will give an incorrect plot. In that sense, why not use the y_axis values themselves?plt.scatter(data["x_axis"], data["y_axis"], s = data["y_axis"]+10)?
– ImportanceOfBeingErnest
Nov 20 '18 at 20:06
I tried your method and it doesnt scale at all. Im not really sure why, your method seems like a fine idea.
– Dane Kania
Nov 20 '18 at 20:25
2
2
Divide by 1700?
– busybear
Nov 20 '18 at 19:52
Divide by 1700?
– busybear
Nov 20 '18 at 19:52
lol yeah i gues i could find some percent to make the values in line to the scale i want. I was looking for a more "pythonic" way of doing it, Thanks though!
– Dane Kania
Nov 20 '18 at 19:54
lol yeah i gues i could find some percent to make the values in line to the scale i want. I was looking for a more "pythonic" way of doing it, Thanks though!
– Dane Kania
Nov 20 '18 at 19:54
I think that's a pretty "pythonic" method. It's straightforward at least. Another thing you might want to consider is
size sets the area and not the diameter.– busybear
Nov 20 '18 at 20:03
I think that's a pretty "pythonic" method. It's straightforward at least. Another thing you might want to consider is
size sets the area and not the diameter.– busybear
Nov 20 '18 at 20:03
I wonder why that approach works at all.
value_counts will produce a series of unique elements of its argument, which are generally less than the number of total elements. So either each value occurs exactly once, then you get all 1 in scale_range, or scale_range does not even have as many elements as x_axis, which might throw an error, or at least will give an incorrect plot. In that sense, why not use the y_axis values themselves? plt.scatter(data["x_axis"], data["y_axis"], s = data["y_axis"]+10)?– ImportanceOfBeingErnest
Nov 20 '18 at 20:06
I wonder why that approach works at all.
value_counts will produce a series of unique elements of its argument, which are generally less than the number of total elements. So either each value occurs exactly once, then you get all 1 in scale_range, or scale_range does not even have as many elements as x_axis, which might throw an error, or at least will give an incorrect plot. In that sense, why not use the y_axis values themselves? plt.scatter(data["x_axis"], data["y_axis"], s = data["y_axis"]+10)?– ImportanceOfBeingErnest
Nov 20 '18 at 20:06
I tried your method and it doesnt scale at all. Im not really sure why, your method seems like a fine idea.
– Dane Kania
Nov 20 '18 at 20:25
I tried your method and it doesnt scale at all. Im not really sure why, your method seems like a fine idea.
– Dane Kania
Nov 20 '18 at 20:25
|
show 2 more comments
0
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2
Divide by 1700?
– busybear
Nov 20 '18 at 19:52
lol yeah i gues i could find some percent to make the values in line to the scale i want. I was looking for a more "pythonic" way of doing it, Thanks though!
– Dane Kania
Nov 20 '18 at 19:54
I think that's a pretty "pythonic" method. It's straightforward at least. Another thing you might want to consider is
sizesets the area and not the diameter.– busybear
Nov 20 '18 at 20:03
I wonder why that approach works at all.
value_countswill produce a series of unique elements of its argument, which are generally less than the number of total elements. So either each value occurs exactly once, then you get all1inscale_range, orscale_rangedoes not even have as many elements asx_axis, which might throw an error, or at least will give an incorrect plot. In that sense, why not use the y_axis values themselves?plt.scatter(data["x_axis"], data["y_axis"], s = data["y_axis"]+10)?– ImportanceOfBeingErnest
Nov 20 '18 at 20:06
I tried your method and it doesnt scale at all. Im not really sure why, your method seems like a fine idea.
– Dane Kania
Nov 20 '18 at 20:25