如何在 Plotly 中将多索引数据框绘制为堆积条形图
How to plot multi-index dataframe as stacked bar chart in Plotly
我有一个下面的数据框，它被进一步处理以创建一个数据透视表 table。现在，我正在尝试在 Plotly 中绘制多索引数据透视表。但在情节上，不知何故它没有取值并显示错误。
我需要在 x 轴上绘制类别 'develop' 和 'developing'，并绘制与这些类别相关的数据。关联的 'employee' 数据应绘制在每个类别中。 'Y 轴必须为 'GDP'，堆栈栏必须为 'cond_cat'。下面是代码供参考。
示例数据帧
import pandas as pd
import numpy as np
s = 200
np.random.seed(365) # so the data is the same each time
df = pd.DataFrame({"Country": np.random.choice(["USA America", "JPY one two", "MEX", "IND", "AUS"], s),
"employee": np.random.choice(["Bob", "Sam", "John", "Tom", "Harry"], s),
"economy_cat": np.random.choice(["developing","develop"], s),
"cond_cat": np.random.choice(["good","bad", 'worse', 'better', 'average'], s),
"gdp": np.random.randint(5, 75, s),
})
df = df[df.Country=='USA America']
# print(df.head())
Country employee economy_cat cond_cat gdp
9 USA America Sam developing better 30
11 USA America Bob developing average 45
21 USA America John develop bad 29
22 USA America Sam develop bad 73
30 USA America Harry develop bad 25
重塑
df_pivot = df.pivot_table(index=['economy_cat','employee'],columns=['cond_cat'],values='gdp',aggfunc='sum')
# print(df_pivot)
cond_cat average bad better good worse
economy_cat employee
develop Bob 6.0 NaN 46.0 NaN NaN
Harry NaN 25.0 9.0 NaN NaN
John 37.0 29.0 NaN NaN NaN
Sam NaN 82.0 NaN NaN 60.0
Tom 48.0 NaN NaN 51.0 NaN
developing Bob 45.0 NaN NaN 45.0 NaN
Harry 75.0 183.0 113.0 NaN NaN
John 16.0 36.0 27.0 67.0 NaN
Sam NaN NaN 30.0 NaN 43.0
Tom 111.0 NaN NaN 77.0 73.0
情节
fig = make_subplots(rows=1, cols=1)
fig.add_trace(
go.Bar(
x= df_pivot["economy_cat","employee"],
y= df_pivot["cond_cat"],marker_color = "#1f77b4",showlegend=False,
marker_line_color = '#1f77b4',
),
row=1,
col=1,
)
fig.add_trace(
go.Bar(
x= df_pivot["economy_cat","employee"],
y= df_pivot["cond_cat"],marker_color = "rgba(255, 0, 0, 0.6)",showlegend=False,
marker_line_color = "rgba(255, 0, 0, 0.6)",
),
row=1,
col=1,
)
fig.update_layout(barmode = 'stack')
fig.show()
绘图时出错
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
e:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3360 try:
-> 3361 return self._engine.get_loc(casted_key)
3362 except KeyError as err:
e:\Anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
e:\Anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: ('economy_cat', 'employee')
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
C:\Users\TRENTO~1.MCK\AppData\Local\Temp/ipykernel_18596/2928341867.py in <module>
14 fig.add_trace(
15 go.Bar(
---> 16 x= df_pivot["economy_cat","employee"],
17 y= df_pivot["cond_cat"],marker_color = "#1f77b4",showlegend=False,
18 marker_line_color = '#1f77b4',
e:\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
3456 if self.columns.nlevels > 1:
3457 return self._getitem_multilevel(key)
-> 3458 indexer = self.columns.get_loc(key)
3459 if is_integer(indexer):
3460 indexer = [indexer]
e:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3361 return self._engine.get_loc(casted_key)
3362 except KeyError as err:
-> 3363 raise KeyError(key) from err
3364
3365 if is_scalar(key) and isna(key) and not self.hasnans:
KeyError: ('economy_cat', 'employee')
如果我没理解错的话，这里是您要查找的完整代码。
重要的是要注意 plotly 期望数据帧列作为轴，而不是多索引，因此，旋转数据帧索引被重置，然后列可以传递给 x=
导入和 DataFrame
import pandas as pd
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.express as px
from itertools import cycle
# beginning with df_pivot from the OP, reset the index
df = df_pivot.reset_index()
# print(df)
cond_cat economy_cat employee average bad better good worse
0 develop Bob 6.0 NaN 46.0 NaN NaN
1 develop Harry NaN 25.0 9.0 NaN NaN
2 develop John 37.0 29.0 NaN NaN NaN
3 develop Sam NaN 82.0 NaN NaN 60.0
4 develop Tom 48.0 NaN NaN 51.0 NaN
5 developing Bob 45.0 NaN NaN 45.0 NaN
6 developing Harry 75.0 183.0 113.0 NaN NaN
7 developing John 16.0 36.0 27.0 67.0 NaN
8 developing Sam NaN NaN 30.0 NaN 43.0
9 developing Tom 111.0 NaN NaN 77.0 73.0
绘图
# data and colors
columns = df.columns[2:]
palette = cycle(px.colors.qualitative.Alphabet)
# palette = cycle(px.colors.sequential.PuBu)
colors = {c:next(palette) for c in columns}
# subplot setup
fig = make_subplots(rows=1, cols=1)
# add bars
for cols in columns:
fig.add_trace(go.Bar(x=[df['economy_cat'], df['employee']],
y = df[cols],
name = cols,
legendgroup = cols,
marker_color = colors[cols],
showlegend = True
), row = 1, col = 1)
fig.update_layout(barmode='stack')
fig.show()
我有一个下面的数据框，它被进一步处理以创建一个数据透视表 table。现在，我正在尝试在 Plotly 中绘制多索引数据透视表。但在情节上，不知何故它没有取值并显示错误。
我需要在 x 轴上绘制类别 'develop' 和 'developing'，并绘制与这些类别相关的数据。关联的 'employee' 数据应绘制在每个类别中。 'Y 轴必须为 'GDP'，堆栈栏必须为 'cond_cat'。下面是代码供参考。
示例数据帧
import pandas as pd
import numpy as np
s = 200
np.random.seed(365) # so the data is the same each time
df = pd.DataFrame({"Country": np.random.choice(["USA America", "JPY one two", "MEX", "IND", "AUS"], s),
"employee": np.random.choice(["Bob", "Sam", "John", "Tom", "Harry"], s),
"economy_cat": np.random.choice(["developing","develop"], s),
"cond_cat": np.random.choice(["good","bad", 'worse', 'better', 'average'], s),
"gdp": np.random.randint(5, 75, s),
})
df = df[df.Country=='USA America']
# print(df.head())
Country employee economy_cat cond_cat gdp
9 USA America Sam developing better 30
11 USA America Bob developing average 45
21 USA America John develop bad 29
22 USA America Sam develop bad 73
30 USA America Harry develop bad 25
重塑
df_pivot = df.pivot_table(index=['economy_cat','employee'],columns=['cond_cat'],values='gdp',aggfunc='sum')
# print(df_pivot)
cond_cat average bad better good worse
economy_cat employee
develop Bob 6.0 NaN 46.0 NaN NaN
Harry NaN 25.0 9.0 NaN NaN
John 37.0 29.0 NaN NaN NaN
Sam NaN 82.0 NaN NaN 60.0
Tom 48.0 NaN NaN 51.0 NaN
developing Bob 45.0 NaN NaN 45.0 NaN
Harry 75.0 183.0 113.0 NaN NaN
John 16.0 36.0 27.0 67.0 NaN
Sam NaN NaN 30.0 NaN 43.0
Tom 111.0 NaN NaN 77.0 73.0
情节
fig = make_subplots(rows=1, cols=1)
fig.add_trace(
go.Bar(
x= df_pivot["economy_cat","employee"],
y= df_pivot["cond_cat"],marker_color = "#1f77b4",showlegend=False,
marker_line_color = '#1f77b4',
),
row=1,
col=1,
)
fig.add_trace(
go.Bar(
x= df_pivot["economy_cat","employee"],
y= df_pivot["cond_cat"],marker_color = "rgba(255, 0, 0, 0.6)",showlegend=False,
marker_line_color = "rgba(255, 0, 0, 0.6)",
),
row=1,
col=1,
)
fig.update_layout(barmode = 'stack')
fig.show()
绘图时出错
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
e:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3360 try:
-> 3361 return self._engine.get_loc(casted_key)
3362 except KeyError as err:
e:\Anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
e:\Anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: ('economy_cat', 'employee')
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
C:\Users\TRENTO~1.MCK\AppData\Local\Temp/ipykernel_18596/2928341867.py in <module>
14 fig.add_trace(
15 go.Bar(
---> 16 x= df_pivot["economy_cat","employee"],
17 y= df_pivot["cond_cat"],marker_color = "#1f77b4",showlegend=False,
18 marker_line_color = '#1f77b4',
e:\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
3456 if self.columns.nlevels > 1:
3457 return self._getitem_multilevel(key)
-> 3458 indexer = self.columns.get_loc(key)
3459 if is_integer(indexer):
3460 indexer = [indexer]
e:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3361 return self._engine.get_loc(casted_key)
3362 except KeyError as err:
-> 3363 raise KeyError(key) from err
3364
3365 if is_scalar(key) and isna(key) and not self.hasnans:
KeyError: ('economy_cat', 'employee')
如果我没理解错的话，这里是您要查找的完整代码。
重要的是要注意 plotly 期望数据帧列作为轴，而不是多索引，因此，旋转数据帧索引被重置，然后列可以传递给 x=
导入和 DataFrame
import pandas as pd
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.express as px
from itertools import cycle
# beginning with df_pivot from the OP, reset the index
df = df_pivot.reset_index()
# print(df)
cond_cat economy_cat employee average bad better good worse
0 develop Bob 6.0 NaN 46.0 NaN NaN
1 develop Harry NaN 25.0 9.0 NaN NaN
2 develop John 37.0 29.0 NaN NaN NaN
3 develop Sam NaN 82.0 NaN NaN 60.0
4 develop Tom 48.0 NaN NaN 51.0 NaN
5 developing Bob 45.0 NaN NaN 45.0 NaN
6 developing Harry 75.0 183.0 113.0 NaN NaN
7 developing John 16.0 36.0 27.0 67.0 NaN
8 developing Sam NaN NaN 30.0 NaN 43.0
9 developing Tom 111.0 NaN NaN 77.0 73.0
绘图
# data and colors
columns = df.columns[2:]
palette = cycle(px.colors.qualitative.Alphabet)
# palette = cycle(px.colors.sequential.PuBu)
colors = {c:next(palette) for c in columns}
# subplot setup
fig = make_subplots(rows=1, cols=1)
# add bars
for cols in columns:
fig.add_trace(go.Bar(x=[df['economy_cat'], df['employee']],
y = df[cols],
name = cols,
legendgroup = cols,
marker_color = colors[cols],
showlegend = True
), row = 1, col = 1)
fig.update_layout(barmode='stack')
fig.show()