i try to run this code, to generate a plot with plotly express.
import plotly.express as px
df = pd.DataFrame([[1,1,1,0,0], [1,1,1,0,1],
[1,1,0,1,0], [0,1,1,1,1]])
##example by plotly
#https://plotly.com/python/facet-plots/
#fig = px.line(df, facet_col="company", facet_col_wrap=2)
fig = px.area(df, facet_col_wrap=2) #works but not as expected
#fig = px.area(df,facet_col="???", facet_col_wrap=2) #should be the solution, but "???" is missing
fig.show(renderer="browser")
the example of plolty (https://plotly.com/python/facet-plots/) has a labeled header ("company"), which is called with the facet_col. id don't know how to insert a label for my df header. i expect to plot the dataframe as in the example from plotly.
You need to get your data frame into appropriate structure for plotly express
unstack() to transform columns into rows in index
reset_index() to make index columns, plus restore original row index with set_index()
now you have a structure to use parameters to px.line()
import plotly.express as px
df = pd.DataFrame([[1,1,1,0,0], [1,1,1,0,1],
[1,1,0,1,0], [0,1,1,1,1]])
px.area(df.unstack().to_frame().reset_index().set_index("level_0"), facet_col="level_1", facet_col_wrap=2)
Related
I have a dataframe with data, the code is below, in which there are 3 columns - date, system and number, building a bar graph in Plotly I get two bars in which I cannot set the sorting by values, they are atomatically sorted by name.
import pandas as pd
import numpy as np
data = [('2022-10-01','Pay1',644), ('2022-10-01','Pay2',1460), ('2022-10-01','Pay3',1221), ('2022-10-01','Pay4',1623),\
('2022-10-01','Pay5',1904), ('2022-10-01','Pay6',1853), ('2022-10-01','Pay7',1826), ('2022-10-01','Pay8',247),\
('2022-10-01','Pay9',713), ('2022-10-01','Pay10',1159), ('2022-10-02','Pay1',755), ('2022-10-02','Pay2',786),\
('2022-10-02','Pay3',623), ('2022-10-02','Pay4',1766), ('2022-10-02','Pay5',1141), ('2022-10-02','Pay6',362),\
('2022-10-02','Pay7',1097), ('2022-10-02','Pay8',655), ('2022-10-02','Pay9',1569), ('2022-10-02','Pay10',796)]
data = pd.DataFrame(data,columns=['date','system','number'])
import plotly.express as px
fig = px.bar(data, x='date', y='number',
color='system')
fig.show()
I want to get a bar that will be sorted by value, from smallest to largest in each case
The expected graph is a stacked graph using the same color for categorical variables, and the order of the graphs is in order of increasing numerical value. To make the categorical variables the same color, create a dictionary of default discrete to maps and system columns. Add a column of colors to each data frame. Extract data frames by date, sort them in numerical order of size, and loop through them row by row.
import plotly.graph_objects as go
import plotly.express as px
colors = px.colors.qualitative.Plotly
system_name = data['system'].unique()
colors_dict = {k:v for k,v in zip(system_name, colors)}
# print(colors_dict)
fig = go.Figure()
dff = data.query('date =="2022-10-01"')
dff = dff.sort_values('number',ascending=False)
dff['color'] = dff['system'].map(colors_dict)
for row in dff.itertuples():
fig.add_trace(go.Bar(x=[row.date], y=[row.number], name=row.system, marker_color=row.color))
fig.update_layout(barmode='stack')
dfm = data.query('date =="2022-10-02"')
dfm = dfm.sort_values('number',ascending=False)
dfm['color'] = dfm['system'].map(colors_dict)
for row in dfm.itertuples():
fig.add_trace(go.Bar(x=[row.date], y=[row.number], name=row.system, marker_color=row.color))
fig.update_layout(barmode='stack')
names = set()
fig.for_each_trace(
lambda trace:
trace.update(showlegend=False)
if (trace.name in names) else names.add(trace.name))
fig.show()
I have a dataframe where each column has many missing values. How can I make a plot where the datapoints in each column are joined with lines, i.e. NAs are ignored, instead of having a choppy plot?
import numpy as np
import pandas as pd
pd.options.plotting.backend = "plotly"
d = pd.DataFrame(data = np.random.choice([np.nan] + list(range(7)), size=(10,3)))
d.plot(markers=True)
One way is to use this for each column:
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, name="linear",
line_shape='linear'))
Are there any better ways to accomplish this?
You can use pandas interpolate. Have demonstrated using plotly express and chained use so underlying data is not changed.
Post comments have amended answer so that markers are not shown for interpreted points.
import numpy as np
import pandas as pd
import plotly.express as px
d = pd.DataFrame(data=np.random.choice([np.nan] + list(range(7)), size=(10, 3)))
px.line(d).update_traces(mode="lines+markers").add_traces(
px.line(d.interpolate(limit_direction="both")).update_traces(showlegend=False).data
)
I have a scatter matrix that I want to change the labels for. On the right-hand, I want to change the blue color 1 to Say Mystery and the red color 2 to say Science. I also want to change the labels of each graph to label their counterpart [Spicy, Savory, and Sweet]. I tried using dict to relabel but then my charts came out wrong.
import plotly.express as px
fig = px.scatter_matrix(df,
dimensions=["Q12_Spicy", "Q12_Sav", "Q12_Sweet", ],color="Q11_Ans"
)
fig.show()
You can create a new column called Q11_Labels that maps 1 to Mystery and 2 to Science from the Q11_Ans column, and pass colors='Q11_Labels' to the px.scatter_matrix function. If you still want the legend to display the original column name, you can pass a dictionary to the labels parameter of the px.scatter_matrix function with labels={"Q11_Labels":"Q11_Ans"}
Then you can extend this dictionary to include the other column name to display name mappings as well, so that [Spicy, Savory, Sweet] are displayed instead of [Q12_Spicy, Q12_Savory, Q12_Sweet].
import numpy as np
import pandas as pd
import plotly.express as px
## recreate random data with the same columns
np.random.seed(42)
df = pd.DataFrame(
np.random.randint(0,100,size=(100, 3)),
columns=["Q12_Spicy", "Q12_Sav", "Q12_Sweet"]
)
df["Q11_Ans"] = np.random.randint(1,3,size=100)
df["Q11_Ans"] = df["Q11_Ans"].astype("category")
df = df.sort_values(by="Q11_Ans")
## remap the values of 1 and 2 to their meanings, then pass this as the color
df["Q11_Labels"] = df["Q11_Ans"].map({1: "Mystery", 2: "Science"})
## pass a dictionary to the labels parameter
fig = px.scatter_matrix(df,
dimensions=["Q12_Spicy", "Q12_Sav", "Q12_Sweet"],color="Q11_Labels",
labels = {"Q12_Spicy":"Spicy","Q12_Sav":"Savory","Q12_Sweet":"Sweet", "Q11_Labels":"Q11_Ans"}
)
fig.show()
Considers the simple data frame below:
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'var3':[1,3,9,6,1,6,3,1,1,3],
'var1':[9,1,2,6,6,5,9,3,1,7],
'var2':[6,6,2,9,8,3,5,4,1,3]})
df
Now, let's plot a set of histograms from this data:
df.hist(layout=(1,3))
plt.show()
Note that the order (from left to right) of the histograms in the figure is different from the order of the columns in the data frame. How to make the histograms obey the order of its data source?
I could not find a way to do that within the df.hist() function. But you can accomplish it with the simple loop below:
fig, ax = plt.subplots(1, len(df.columns), figsize=(3*len(df.columns), 3))
for i, var in enumerate(df):
df[var].hist(ax=ax[i])
ax[i].set_title(var)
plt.show()
Result:
I like #foglerit's answer, but here's another workaround solution:
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'var3':[1,3,9,6,1,6,3,1,1,3],
'var1':[9,1,2,6,6,5,9,3,1,7],
'var2':[6,6,2,9,8,3,5,4,1,3]})
columns = df.columns # save original column names
columns_temp = [] # create temporary column names, numbered
for i, col in enumerate(df.columns):
columns_temp.append('(' + str(i+1) + ') ' + str(col))
df.columns = columns_temp
df.hist(layout=(1,3)) # now the column order is not messed up
df.columns = columns # reassign original column names
I have the following Pandas data frame and I'm trying to create a boxplot of the "dur" value for both client and server organized by qdepth (qdepth on x-axis, duration on y-axis, with two variables client and server). It seems like I need to get client and serveras columns. I haven't been able to figure this out trying combinations ofunstackandreset_index`.
Here's some dummy data I recreated since you didn't post yours aside from an image:
qdepth,mode,runid,dur
1,client,0x1b7bd6ef955979b6e4c109b47690c862,7.0
1,client,0x45654ba030787e511a7f0f0be2db21d1,30.0
1,server,0xb760550f302d824630f930e3487b4444,19.0
1,server,0x7a044242aec034c44e01f1f339610916,95.0
2,client,0x51c88822b28dfa006bf38603d74f9911,15.0
2,client,0xd5a9028fddf9a400fd8513edbdc58de0,49.0
2,server,0x3943710e587e3932adda1cad8eaf2aeb,30.0
2,server,0xd67650fd984a48f2070de426e0a942b0,93.0
Load the data: df = pd.read_clipboard(sep=',', index_col=[0,1,2])
Option 1:
df.unstack(level=1).boxplot()
Option 2:
df.unstack(level=[0,1]).boxplot()
Option 3:
Using seaborn:
import seaborn as sns
sns.boxplot(x="qdepth", hue="mode", y="dur", data=df.reset_index(),)
Update:
To answer your comment, here's a very approximate way (could be used as a starting point) to recreate the seaborn option using only pandas and matplotlib:
fig, ax = plt.subplots(nrows=1,ncols=1, figsize=(12,6))
#bp = df.unstack(level=[0,1])['dur'].boxplot(ax=ax, return_type='dict')
bp = df.reset_index().boxplot(column='dur',by=['qdepth','mode'], ax=ax, return_type='dict')['dur']
# Now fill the boxes with desired colors
boxColors = ['darkkhaki', 'royalblue']
numBoxes = len(bp['boxes'])
for i in range(numBoxes):
box = bp['boxes'][i]
boxX = []
boxY = []
for j in range(5):
boxX.append(box.get_xdata()[j])
boxY.append(box.get_ydata()[j])
boxCoords = list(zip(boxX, boxY))
# Alternate between Dark Khaki and Royal Blue
k = i % 2
boxPolygon = mpl.patches.Polygon(boxCoords, facecolor=boxColors[k])
ax.add_patch(boxPolygon)
plt.show()