My attempt to use FacetGrid in Seaborn does not produces the expected results.
Moreover, I would like to control the white space in the grid.
My data and code is the following:
toy.to_json()
'{"has_cus_id_but_not_acc_id":{"0":0,"1":0,"2":0,"3":0,"4":0,"5":0,"6":0,"7":0,"8":0,"9":0,"10":0,"11":0,"12":0,"13":0,"14":0,"15":0,"16":0,"17":0,"18":1,"19":0,"20":0,"21":0,"22":1,"23":0,"24":0,"25":1,"26":0,"27":1,"28":0,"29":1,"30":0,"31":1,"32":0,"33":1,"34":0,"35":1,"36":0,"37":1,"38":0,"39":0,"40":1,"41":1,"42":0,"43":1,"44":0,"45":1,"46":0,"47":1,"48":0,"49":1,"50":0,"51":1,"52":0,"53":1,"54":0,"55":1,"56":0,"57":1,"58":0,"59":1,"60":0,"61":1,"62":0,"63":1,"64":0,"65":1,"66":0,"67":1,"68":0,"69":1,"70":0,"71":1,"72":0,"73":1,"74":0,"75":1,"76":0,"77":0,"78":1,"79":0,"80":1,"81":0,"82":0,"83":1,"84":0,"85":1},"reg_year":{"0":2014.0,"1":2014.0,"2":2014.0,"3":2014.0,"4":2014.0,"5":2014.0,"6":2014.0,"7":2014.0,"8":2015.0,"9":2015.0,"10":2015.0,"11":2015.0,"12":2015.0,"13":2015.0,"14":2015.0,"15":2015.0,"16":2015.0,"17":2016.0,"18":2016.0,"19":2016.0,"20":2016.0,"21":2016.0,"22":2016.0,"23":2016.0,"24":2016.0,"25":2016.0,"26":2016.0,"27":2016.0,"28":2016.0,"29":2016.0,"30":2016.0,"31":2016.0,"32":2016.0,"33":2016.0,"34":2016.0,"35":2016.0,"36":2016.0,"37":2016.0,"38":2017.0,"39":2017.0,"40":2017.0,"41":2017.0,"42":2017.0,"43":2017.0,"44":2017.0,"45":2017.0,"46":2017.0,"47":2017.0,"48":2017.0,"49":2017.0,"50":2017.0,"51":2017.0,"52":2017.0,"53":2017.0,"54":2017.0,"55":2017.0,"56":2017.0,"57":2017.0,"58":2017.0,"59":2017.0,"60":2018.0,"61":2018.0,"62":2018.0,"63":2018.0,"64":2018.0,"65":2018.0,"66":2018.0,"67":2018.0,"68":2018.0,"69":2018.0,"70":2018.0,"71":2018.0,"72":2018.0,"73":2018.0,"74":2018.0,"75":2018.0,"76":2018.0,"77":2018.0,"78":2018.0,"79":2018.0,"80":2018.0,"81":2018.0,"82":2019.0,"83":2019.0,"84":2019.0,"85":2019.0},"reg_month":{"0":3.0,"1":5.0,"2":6.0,"3":7.0,"4":9.0,"5":10.0,"6":11.0,"7":12.0,"8":1.0,"9":3.0,"10":5.0,"11":6.0,"12":7.0,"13":8.0,"14":9.0,"15":11.0,"16":12.0,"17":1.0,"18":1.0,"19":2.0,"20":3.0,"21":4.0,"22":4.0,"23":5.0,"24":6.0,"25":6.0,"26":7.0,"27":7.0,"28":8.0,"29":8.0,"30":9.0,"31":9.0,"32":10.0,"33":10.0,"34":11.0,"35":11.0,"36":12.0,"37":12.0,"38":1.0,"39":2.0,"40":2.0,"41":3.0,"42":4.0,"43":4.0,"44":5.0,"45":5.0,"46":6.0,"47":6.0,"48":7.0,"49":7.0,"50":8.0,"51":8.0,"52":9.0,"53":9.0,"54":10.0,"55":10.0,"56":11.0,"57":11.0,"58":12.0,"59":12.0,"60":1.0,"61":1.0,"62":2.0,"63":2.0,"64":3.0,"65":3.0,"66":4.0,"67":4.0,"68":5.0,"69":5.0,"70":6.0,"71":6.0,"72":7.0,"73":7.0,"74":8.0,"75":8.0,"76":9.0,"77":10.0,"78":10.0,"79":11.0,"80":11.0,"81":12.0,"82":1.0,"83":1.0,"84":2.0,"85":2.0},"Total_Revenue":{"0":35852.02,"1":2623.97,"2":3526.67,"3":21466.71,"4":72784.1200000003,"5":103921.2899999999,"6":10852.87,"7":16522.07,"8":7443.76,"9":68962.1600000002,"10":10956.38,"11":193856.8799999985,"12":110766.6099999997,"13":123861.8599999987,"14":2722.34,"15":303488.6900000007,"16":6876.58,"17":17729.5,"18":4687.93,"19":26914.06,"20":2228.12,"21":15708.93,"22":859.58,"23":19164.89,"24":163164.4799999995,"25":33180.7300000001,"26":10033.01,"27":1114.48,"28":462613.2900000042,"29":9822.95,"30":70901.4400000003,"31":22370.29,"32":46711.8900000002,"33":2335.02,"34":7259.28,"35":11.83,"36":13590.51,"37":7677.77,"38":282.01,"39":358522.7900000003,"40":5844.0,"41":7027.28,"42":1908.71,"43":4032.35,"44":11072.6,"45":3973.15,"46":30706.23,"47":2644.13,"48":23831.75,"49":670.12,"50":6949.54,"51":4687.7,"52":9672.69,"53":7333.01,"54":12814.33,"55":689.39,"56":6962.86,"57":2283.16,"58":1259.5,"59":224.84,"60":12812.12,"61":247.68,"62":25452.65,"63":1245.02,"64":24211.36,"65":5255.25,"66":28402.76,"67":9148.55,"68":14822.61,"69":345.37,"70":12408.13,"71":989.93,"72":10601.33,"73":730.32,"74":169020.5000000001,"75":697.54,"76":3862038.6799997138,"77":6148750.9899984254,"78":194.06,"79":2379382.4500000761,"80":1174.11,"81":1729567.9000000793,"82":889650.029999995,"83":95.8,"84":415996.6999999974,"85":654.78}}'
g = sns.FacetGrid(toy, col='has_cus_id_but_not_acc_id', hue='reg_year')
g.map(sns.barplot, 'reg_month', 'Total_Revenue')
g.add_legend();
If I use bar in pyplot I get this:
g = sns.FacetGrid(toy, col='has_cus_id_but_not_acc_id', hue='reg_year')
g.map(plt.bar, 'reg_month', 'Total_Revenue')
g.add_legend();
Again, I would like to be able to define the white space of the grid.
In addition I would not like to have the bars stacked one over the other but rather one next to the other.
Some values of the year 2018 are really large compared to the any of the values where has_cus_id_but_not_acc_id is 1. Hence the right plot is almost empty. It might make sense to use a logarithmic scale.
Now you have 6 years, so each month would need to show 6 bars next to each other. That will make bars pretty small and does not let the chart be easily readable. Still it's possible.
The following does not use seaborn, but pandas and matplotlib:
import matplotlib.pyplot as plt
import pandas as pd
toy = '{"has_cus_id_but_not_acc_id":{"0":0,"1":0,"2":0,"3":0,"4":0,"5":0,"6":0,"7":0,"8":0,"9":0,"10":0,"11":0,"12":0,"13":0,"14":0,"15":0,"16":0,"17":0,"18":1,"19":0,"20":0,"21":0,"22":1,"23":0,"24":0,"25":1,"26":0,"27":1,"28":0,"29":1,"30":0,"31":1,"32":0,"33":1,"34":0,"35":1,"36":0,"37":1,"38":0,"39":0,"40":1,"41":1,"42":0,"43":1,"44":0,"45":1,"46":0,"47":1,"48":0,"49":1,"50":0,"51":1,"52":0,"53":1,"54":0,"55":1,"56":0,"57":1,"58":0,"59":1,"60":0,"61":1,"62":0,"63":1,"64":0,"65":1,"66":0,"67":1,"68":0,"69":1,"70":0,"71":1,"72":0,"73":1,"74":0,"75":1,"76":0,"77":0,"78":1,"79":0,"80":1,"81":0,"82":0,"83":1,"84":0,"85":1},"reg_year":{"0":2014.0,"1":2014.0,"2":2014.0,"3":2014.0,"4":2014.0,"5":2014.0,"6":2014.0,"7":2014.0,"8":2015.0,"9":2015.0,"10":2015.0,"11":2015.0,"12":2015.0,"13":2015.0,"14":2015.0,"15":2015.0,"16":2015.0,"17":2016.0,"18":2016.0,"19":2016.0,"20":2016.0,"21":2016.0,"22":2016.0,"23":2016.0,"24":2016.0,"25":2016.0,"26":2016.0,"27":2016.0,"28":2016.0,"29":2016.0,"30":2016.0,"31":2016.0,"32":2016.0,"33":2016.0,"34":2016.0,"35":2016.0,"36":2016.0,"37":2016.0,"38":2017.0,"39":2017.0,"40":2017.0,"41":2017.0,"42":2017.0,"43":2017.0,"44":2017.0,"45":2017.0,"46":2017.0,"47":2017.0,"48":2017.0,"49":2017.0,"50":2017.0,"51":2017.0,"52":2017.0,"53":2017.0,"54":2017.0,"55":2017.0,"56":2017.0,"57":2017.0,"58":2017.0,"59":2017.0,"60":2018.0,"61":2018.0,"62":2018.0,"63":2018.0,"64":2018.0,"65":2018.0,"66":2018.0,"67":2018.0,"68":2018.0,"69":2018.0,"70":2018.0,"71":2018.0,"72":2018.0,"73":2018.0,"74":2018.0,"75":2018.0,"76":2018.0,"77":2018.0,"78":2018.0,"79":2018.0,"80":2018.0,"81":2018.0,"82":2019.0,"83":2019.0,"84":2019.0,"85":2019.0},"reg_month":{"0":3.0,"1":5.0,"2":6.0,"3":7.0,"4":9.0,"5":10.0,"6":11.0,"7":12.0,"8":1.0,"9":3.0,"10":5.0,"11":6.0,"12":7.0,"13":8.0,"14":9.0,"15":11.0,"16":12.0,"17":1.0,"18":1.0,"19":2.0,"20":3.0,"21":4.0,"22":4.0,"23":5.0,"24":6.0,"25":6.0,"26":7.0,"27":7.0,"28":8.0,"29":8.0,"30":9.0,"31":9.0,"32":10.0,"33":10.0,"34":11.0,"35":11.0,"36":12.0,"37":12.0,"38":1.0,"39":2.0,"40":2.0,"41":3.0,"42":4.0,"43":4.0,"44":5.0,"45":5.0,"46":6.0,"47":6.0,"48":7.0,"49":7.0,"50":8.0,"51":8.0,"52":9.0,"53":9.0,"54":10.0,"55":10.0,"56":11.0,"57":11.0,"58":12.0,"59":12.0,"60":1.0,"61":1.0,"62":2.0,"63":2.0,"64":3.0,"65":3.0,"66":4.0,"67":4.0,"68":5.0,"69":5.0,"70":6.0,"71":6.0,"72":7.0,"73":7.0,"74":8.0,"75":8.0,"76":9.0,"77":10.0,"78":10.0,"79":11.0,"80":11.0,"81":12.0,"82":1.0,"83":1.0,"84":2.0,"85":2.0},"Total_Revenue":{"0":35852.02,"1":2623.97,"2":3526.67,"3":21466.71,"4":72784.1200000003,"5":103921.2899999999,"6":10852.87,"7":16522.07,"8":7443.76,"9":68962.1600000002,"10":10956.38,"11":193856.8799999985,"12":110766.6099999997,"13":123861.8599999987,"14":2722.34,"15":303488.6900000007,"16":6876.58,"17":17729.5,"18":4687.93,"19":26914.06,"20":2228.12,"21":15708.93,"22":859.58,"23":19164.89,"24":163164.4799999995,"25":33180.7300000001,"26":10033.01,"27":1114.48,"28":462613.2900000042,"29":9822.95,"30":70901.4400000003,"31":22370.29,"32":46711.8900000002,"33":2335.02,"34":7259.28,"35":11.83,"36":13590.51,"37":7677.77,"38":282.01,"39":358522.7900000003,"40":5844.0,"41":7027.28,"42":1908.71,"43":4032.35,"44":11072.6,"45":3973.15,"46":30706.23,"47":2644.13,"48":23831.75,"49":670.12,"50":6949.54,"51":4687.7,"52":9672.69,"53":7333.01,"54":12814.33,"55":689.39,"56":6962.86,"57":2283.16,"58":1259.5,"59":224.84,"60":12812.12,"61":247.68,"62":25452.65,"63":1245.02,"64":24211.36,"65":5255.25,"66":28402.76,"67":9148.55,"68":14822.61,"69":345.37,"70":12408.13,"71":989.93,"72":10601.33,"73":730.32,"74":169020.5000000001,"75":697.54,"76":3862038.6799997138,"77":6148750.9899984254,"78":194.06,"79":2379382.4500000761,"80":1174.11,"81":1729567.9000000793,"82":889650.029999995,"83":95.8,"84":415996.6999999974,"85":654.78}}'
df = pd.read_json(toy)
df['reg_year'].astype(int)
u = df["has_cus_id_but_not_acc_id"].unique()
y = df['reg_year'].unique()
fig, axes = plt.subplots(1,len(u), sharey=True)
axes[0].set_yscale("log")
for ax, (n, grp) in zip(axes.flat, df.groupby("has_cus_id_but_not_acc_id")):
piv = grp.pivot('reg_month', 'reg_year', 'Total_Revenue')
empty = pd.DataFrame(index=range(1,12), columns=y)
empty.combine_first(piv).plot.bar(ax=ax, width=0.8, legend=False)
axes[1].legend()
plt.show()
Related
I have a bar chart with a lot of columns (around 100). I want to show only some of the bar labels (they are ordered in such a way that this is a perfectly reasonable way to present the data). Is there a simple way to do this, say show every 3rd or 5th label? I know I can manually pull together the list, but I figure there's likely an elegant option.
import matplotlib.pyplot as plt
import numpy as np
data = np.random.rand(100)
groupings = np.arange(0,100)
x_pos = [i for i, _ in enumerate(groupings)]
plt.bar(x_pos,data)
plt.xticks(x_pos,groupings)
I am very new to python and plotly.express, and I find it very confusing...
I am trying to use the principle of adding different traces to my figure, using example code shown here https://plotly.com/python/line-charts/, Line Plot Modes, #Create traces.
BUT I get my data from a .CSV file.
import plotly.express as px
import plotly as plotly
import plotly.graph_objs as go
import pandas as pd
data = pd.read_csv(r"C:\Users\x.csv")
fig = px.scatter(data, x="Time", y="OD", color="C-source", size="C:A 1 ratio")
fig = px.line(data, x="Time", y="OD", color="C-source")
fig.show()
The above lines produces scatter/line plots with the correct data, but the data is mixed together. I have data from 2 different sources marked by a column named "Strain" in my .csv file that I would like the chart to reflect.
Is the traces option a possible way to do it, or is there another way?
You can add traces using an Express plot by using .select_traces(). Something like:
fig.add_traces(
list(px.line(...).select_traces())
)
Note the need to convert to list, since .select_traces() returns a generator.
It looks like you probably want the lines with the scatter dots as well on a single plot?
You're setting fig to equal px.scatter() and then setting (changing) it to equal px.line(). When set to line, the scatter plot is overwritten.
You're already importing graph objects so you can use add_trace with go, something like this:
fig.add_trace(go.Scatter(x=data["Time"], y=data["OD"], mode='markers', marker=dict(color=data["C-source"], size=data["C:A 1 ratio"])))
Depending on how your data is set up, you may need to add each C-source separately doing something like:
x=data.query("C-source=='Term'")["Time"], ... , name='Term'`
Here's a few references with examples and options you can use to set up your scatter:
Scatter plot examples
Marker styles
Scatter arguments and attributes
You can use the apporach stated in Plotly: How to combine scatter and line plots using Plotly Express?
fig3 = go.Figure(data=fig1.data + fig2.data)
or a more convenient and scalable approach:
fig1.data and fig2.data are common tuples that hold all the info needed for a plot and the + just concatenates them.
# this will hold all figures until they are combined
all_figures = []
# data_collection: dictionary with Pandas dataframes
for df_label in data_collection:
df = data_collection[df_label]
fig = px.line(df, x='Date', y=['Value'])
all_figures.append(fig)
import operator
import functools
# now you can concatenate all the data tuples
# by using the programmatic add operator
fig3 = go.Figure(data=functools.reduce(operator.add, [_.data for _ in all_figures]))
fig3.show()
thanks for taking the time to help me out. I ended up with two solutions that worked, of which using "facet_col" to divide the plot into two subplots (1 for each strain) was the most simple solution.
https://plotly.com/python/axes/
Thanks. this worked for me also where Fig_Set_B is a list of scatter plots
# create a tuple of first line plots in first 6 plots from plot set Fig_Set_B`
fig_combined = go.Figure(data= tuple(Fig_Set_B[x].data[0] for x in range(6)) )
fig_combined.show()
I am trying to make v1 as blue, v2 as orange, v3 green and v4 as light grey
I tried going through documentation but cannot understand how to define color in piechart. Thank you for help.
I am using few line of codes of generate a piechart
where vol1 = v1,v2,v3,v4
plt.pie(vol1,labels = vollabels, autopct="%0.2f%%")
plt.legend(title="Normalized Volumes",loc="upper left", fontsize=14)
plt.axis
plt.show()
If you want to have control over which colors your pie chart contains, while at the same time not fall out of matplotlib's convenient handling of colour maps, you might want to have a look at documentation example Nested pie charts. Extracted highlights:
import matplotlib.pyplot as plt
import numpy as np
Retrieve a named colour map and "hand-pick", using a numbered range, suitable colors. The index picking in inner_colors matches hues for a larger numbers of data points in the inner circle:
cmap = plt.get_cmap("tab20c")
outer_colors = cmap(np.arange(3)*4)
inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))
The actual plotting, including some customisation, is then straightforward:
fig, ax = plt.subplots()
size = 0.3
vals = np.array([[60., 32.], [37., 40.], [29., 10.]])
ax.pie(vals.sum(axis=1), radius=1, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w'))
ax.pie(vals.flatten(), radius=1-size, colors=inner_colors,
wedgeprops=dict(width=size, edgecolor='w'))
Bonus content in the linked location: how to achieve the same result using a bar plot, but using polar coordinates. That way, one has more flexibility over the exact design, if one's goals diverge from the defaults assumed in pie.
import pandas as pd
import altair as alt
dicta = {
'date':['2019-06-29', '2019-06-30', '2019-07-01', '2019-07-02', '2019-07-03'],
'amount':[-9.35, -6.42, -13.55, -12.88, -12.24] }
dataset = pd.DataFrame(dicta)
alt.Chart(dataset).mark_bar().encode(
x = "date:T",
y = "amount:N"
)
I'm not sure why this generates horizontal bars, instead of vertical bars by default.
How can I change it? I would like to see a bar per day, up to the amount for the day.
Found the answer. I encoded the numerical column with N. But this is for nominal data, and got Altair confused. Need to use Q/ Quantitative
I would like to specify the color of particular observations using seaborn catplot. In a made up exemple:
import seaborn as sns
import random as r
name_list=['pepe','Fabrice','jim','Michael']
country_list=['spain','France','uk','Uruguay']
favourite_color=['green','blue','red','white']
df=pd.DataFrame({'name':[r.choice(name_list) for n in range(100)],
'country':[r.choice(country_list) for n in range(100)],
'fav_color':[r.choice(favourite_color) for n in range(100)],
'score':np.random.rand(100),
})
sns.catplot(x='fav_color',
y='score',
col='country',
col_wrap=2,
data=df,
kind='swarm')
I would like to colour (or mark in another distinctive way, it could be the marker) all the observations with the name 'pepe'. How I could do that ? The other colors I dont mind, it would be better if they are all the same.
You can achieve the results you want by adding a boolean column to the dataframe and using it as the hue parameter of the catplot() call. This way you will obtain the results with two colours (one for pepe observations and one for the rest). Results can be seen here:
Also, the parameter legend=False should be set since otherwise the legend for is_pepe will appear on the side.
The code would be as below:
df['is_pepe'] = df['name'] == 'pepe'
ax = sns.catplot(x='fav_color',
y='score',
col='country',
col_wrap=2,
data=df,
kind='swarm',
hue='is_pepe',
legend=False)
Furthermore, you can specify the two colours you want for the two kinds of observations (pepe and not-pepe) using the parameter palette and the top-level function sns.color_palette() as shown below:
ax = sns.catplot(x='fav_color',
y='score',
col='country',
col_wrap=2,
data=df,
kind='swarm',
hue='is_pepe',
legend=False,
palette=sns.color_palette(['green', 'blue']))
Obtaining this results: