I have some problems sorting a multicategorial chart.
Some example code.
import pandas as pd
import plotly.graph_objects as go
data = [
[0, "Born", 4, "Rhino"], # commenting this line will also reverse sub category sorting
[0, "Died", -1, "Rhino"],
[1, "Born", 4, "Lion"],
[1, "Died", -1, "Lion"],
[2, "Born", 12, "Rhino"],
[2, "Died", -5, "Lion"],
]
z_data = list(zip(*data))
df = pd.DataFrame({
"tick": z_data[0],
"category": z_data[1],
"value": z_data[2],
"type": z_data[3],
})
df = df.sort_values(by=['tick', 'category', 'value', 'type'])
print(df)
fig = go.Figure()
for t in df.type.unique():
plot_df = df[df.type == t]
fig.add_trace(go.Bar(
x=[plot_df.tick, plot_df.category],
y=abs(plot_df.value),
name=t,
))
fig.update_layout({
'barmode': 'stack',
'xaxis': {
'title_text': "Tick",
'tickangle': -90,
},
'yaxis': {
'title_text': "Value",
},
})
fig.write_html(str("./diagram.html"))
As you can see the tick 2 is before tick 1. This happens because the 'Rhino' is the first in type list, which will create the tick 0 and 2. The lion bars are added after with tick 1. But how can i sort the bars properly now?
PS. 'barmode': 'stack'
is on purpose. Even if it is not used in this test example.
I'm able to fix the tick but not the born/died order. I'm planning to plot row by row so I need to play with showlegend
import pandas as pd
import plotly.graph_objects as go
data = [
[0, "Born", 4, "Rhino"], # commenting this line will also reverse sub category sorting
[0, "Died", -1, "Rhino"],
[1, "Born", 4, "Lion"],
[1, "Died", -1, "Lion"],
[2, "Born", 12, "Rhino"],
[2, "Died", -5, "Lion"],
]
# you don't really need to zip here
df = pd.DataFrame(data, columns=["tick", "category", "value", "type"])
df["value"] = df["value"].abs()
In case you have more types there are answer here that can help you. Check doc
color_diz = {"Rhino": "blue", "Lion": "red"}
df["color"] = df["type"].map(color_diz)
Here I want to show the legend for the first occurrence of every type
grp = df.groupby("type")\
.apply(lambda x: x.index.min())\
.reset_index(name="idx")
df = pd.merge(df, grp, on=["type"], how="left")
df["showlegend"] = df.index == df["idx"]
print(df)
tick category value type color idx showlegend
0 0 Born 4 Rhino blue 0 True
1 0 Died 1 Rhino blue 0 False
2 1 Born 4 Lion red 2 True
3 1 Died 1 Lion red 2 False
4 2 Born 12 Rhino blue 0 False
5 2 Died 5 Lion red 2 False
fig = go.Figure()
for i, row in df.iterrows():
fig.add_trace(
go.Bar(x=[[row["tick"]], [row["category"]]],
y=[row["value"]],
name=row["type"],
marker_color=row["color"],
showlegend=row["showlegend"],
legendgroup=row["type"] # Fix legend
))
fig.update_layout({
'barmode': 'stack',
'xaxis': {
'title_text': "Tick",
'tickangle': -90,
},
'yaxis': {
'title_text': "Value",
},
})
fig.show()
EDIT
If you have more type
you could use the following trick.
First I generate different types
import string
import numpy as np
import pandas as pd
import plotly.express as px
df = pd.DataFrame({"type":np.random.choice(list(string.ascii_lowercase), 100)})
Then I pick a color sequence from doc and put them on a dictionary
color_dict = {k:v for k,v in enumerate(px.colors.qualitative.Plotly)}
Then I put the unique type
on a dataframe
df_col = pd.DataFrame({"type": df["type"].unique()})
and I assign each of them a color according to its index
df_col["color"] = (df_col.index%len(color_dict)).map(color_dict)
Finally I merge to the original df
df = pd.merge(df, df_col, on=["type"], how="left")
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