Add a slider to plotly that dynamically changes a column of data frame that is displayed - pandas

Minimal working example:
import pandas as pd
import plotly.express as px
A = [10,20,30,40,50,60]
B = [40,50,60,10,20,30]
data = pd.DataFrame({"A":A,"B":B})
alpha=0.5
data["Parameter"]= alpha*data["A"] +(1-alpha)*data["B"]
fig = px.scatter(
data, x="A",y="B",color="Parameter"
)
fig.show()
I would like to have a slider for alpha in plotly graph. I looked at the documentation but only found a slider for a fixed column with constant values.

fig = px.scatter(data, x="A", y="B", color="Parameter", animation_frame='Parameter')
fig["layout"].pop("updatemenus")
fig.update_xaxes(range=[0, 100])
fig.show('browser')

Related

Equivalent of Hist()'s Layout hyperparameter in Sns.Pairplot?

Am trying to find hist()'s figsize and layout parameter for sns.pairplot().
I have a pairplot that gives me nice scatterplots between the X's and y. However, it is oriented horizontally and there is no equivalent layout parameter to make them vertical to my knowledge. 4 plots per row would be great.
This is my current sns.pairplot():
sns.pairplot(X_train,
x_vars = X_train.select_dtypes(exclude=['object']).columns,
y_vars = ["SalePrice"])
This is what I would like it to look like: Source
num_mask = train_df.dtypes != object
num_cols = train_df.loc[:, num_mask[num_mask == True].keys()]
num_cols.hist(figsize = (30,15), layout = (4,10))
plt.show()
What you want to achieve isn't currently supported by sns.pairplot, but you can use one of the other figure-level functions (sns.displot, sns.catplot, ...). sns.lmplot creates a grid of scatter plots. For this to work, the dataframe needs to be in "long form".
Here is a simple example. sns.lmplot has parameters to leave out the regression line (fit_reg=False), to set the height of the individual subplots (height=...), to set its aspect ratio (aspect=..., where the subplot width will be height times aspect ratio), and many more. If all y ranges are similar, you can use the default sharey=True.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# create some test data with different y-ranges
np.random.seed(20230209)
X_train = pd.DataFrame({"".join(np.random.choice([*'uvwxyz'], np.random.randint(3, 8))):
np.random.randn(100).cumsum() + np.random.randint(100, 1000) for _ in range(10)})
X_train['SalePrice'] = np.random.randint(10000, 100000, 100)
# convert the dataframe to long form
# 'SalePrice' will get excluded automatically via `melt`
compare_columns = X_train.select_dtypes(exclude=['object']).columns
long_df = X_train.melt(id_vars='SalePrice', value_vars=compare_columns)
# create a grid of scatter plots
g = sns.lmplot(data=long_df, x='SalePrice', y='value', col='variable', col_wrap=4, sharey=False)
g.set(ylabel='')
plt.show()
Here is another example, with histograms of the mpg dataset:
import matplotlib.pyplot as plt
import seaborn as sns
mpg = sns.load_dataset('mpg')
compare_columns = mpg.select_dtypes(exclude=['object']).columns
mpg_long = mpg.melt(value_vars=compare_columns)
g = sns.displot(data=mpg_long, kde=True, x='value', common_bins=False, col='variable', col_wrap=4, color='crimson',
facet_kws={'sharex': False, 'sharey': False})
g.set(xlabel='')
plt.show()

Is there any way to show gray color to states which are not having any data in Plotly map?

I need to show gray color to the states which do not have any data in Plotly.
Sample csv file is: (This states have data)
States which are not having data are: (I have filled the missing values as -1
The current plots generated are: ( I need to show gray color to the states with missing data.
Thanks!
Your solution is to use custom colorscale in combination with
import plotly.express as px
px.choropleth_mapbox
The following is an example on how to use custom colorscale:
import plotly.plotly as py
import plotly.graph_objs as go
import numpy as np
import copy
import pandas as pd
# Read data from a csv
z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv')
z=z_data.values.copy()
# Compute surface color with nan's
surfacecolor = z.copy()
surfacecolor[-10:, -10:] = np.nan
# Replace nans with -100
surfacecolor[np.isnan(surfacecolor)] = -100
# Build surface trace
data = [
go.Surface(
z=z,
surfacecolor=surfacecolor,
cmin = -5,
cmax = 350,
colorscale=[[0, 'gray'],
[0.01, 'gray'],
[0.01, 'blue'],
[1, 'red']]
)
]
# Build layout
layout = go.Layout(
title='Mt Bruno Elevation',
autosize=False,
width=500,
height=500,
margin=dict(
l=65,
r=50,
b=65,
t=90
)
)
fig = go.FigureWidget(data=data, layout=layout)
fig
A similar question has been solved by the plotly community forum.
Please find the plotly documentation on how to define custom colorscales.
Hope this solves your issue!

How to plot frequency distribution graph using Matplotlib?

I trust you are doing well. I am using a data frame in which there are two columns screens and it's frequency. I am trying to find out the relationship between the screen and the frequency of the appearance of the screens. Now I want to know, for all screens what are all of the frequencies as sort of a summary graph. Imagine putting all of those frequencies into an array, and wanting to study the distribution in that array. Below is my code that I have tried so far:
data = pd.read_csv('frequency_list.csv')
new_columns = data.columns.values
new_columns[1] = 'frequency'
data.columns = new_columns
import matplotlib.pyplot as plt
%matplotlib inline
dataset = data.head(10)
dataset.plot(x = "screen", y = "frequency", kind = "bar")
plt.show()
col_one_list = unpickled_df['screen'].tolist()
col_one_arr = unpickled_df['screen'].head(10).to_numpy()
plt.hist(col_one_arr) #gives you a histogram of your array 'a'
plt.show() #finishes out the plot
Below is the screenshot of my data frame containing screen as one column and frequency as another. Can you help me to find out a way to plot a frequency distribution graph? Thanks in advance.
Will a bar plot work? Here's an example:
import pandas as pd
import matplotlib.pyplot as plt
freq = [102,98,56,117]
screen = ['A','B','C','D']
df = pd.DataFrame(list(zip(screen, freq)), columns=['screen', 'freq'])
plt.bar(df.screen,df.freq)
plt.xlabel('x')
plt.ylabel('count')
plt.show()

pandas dataframe bar plot put space between bars

So I want my image look like this
But now my image look like this
How do I reduce the space between bars without making the bar width into 1?
Here is my code:
plot=repeat.loc['mean'].plot(kind='bar',rot=0,alpha=1,cmap='Reds',
yerr=repeat.loc['std'],error_kw=dict(elinewitdh=0.02,ecolor='grey'),
align='center',width=0.2,grid=None)
plt.ylabel('')
plt.grid(False)
plt.title(cell,ha='center')
plt.xticks([])
plt.yticks([])
plt.ylim(0,120)
plt.tight_layout()`
make the plot from scratch if the toplevel functions from pandas or seaborn do not give you the desired result! :)
import seaborn.apionly as sns
import scipy as sp
import matplotlib.pyplot as plt
# some fake data
data = sp.randn(10,10) + 1
data = data[sp.argsort(sp.average(data,axis=1))[::-1],:]
avg = sp.average(data,axis=1)
std = sp.std(data,axis=1)
# a practical helper from seaborn to quickly generate the colors
colors = sns.color_palette('Reds',n_colors = data.shape[0])
fig, ax = plt.subplots()
pos = range(10)
ax.bar(pos,avg,width=1)
for col,patch in zip(colors,ax.patches):
patch.set_facecolor(col)
patch.set_edgecolor('k')
for i,p in enumerate(pos):
ax.plot([p,p],[avg[i],avg[i]+std[i]],color='k',lw=2, zorder=-1)

Interactively annotating points in scatter plot using Bokeh

I'm trying to use Bokeh to build an interactive tool that allows a user to select a subset of points from a scatter plot and to subsequently label or annotate those points. Ideally, the user-provided input would update a "label" field for that sample's row in a dataframe.
The code below allows the user to select the points, but how do I make it so that they can then label those selected points from a text-input widget e.g. text = TextInput(value="default", title="Label:")
, and in so doing, change the "label" field for that sample in the dataframe?
import pandas as pd
import numpy as np
from bokeh.plotting import figure, output_file, show, ColumnDataSource
from bokeh.models import HoverTool
from bokeh.models.widgets import TextInput
data = pd.DataFrame()
data["x"] = np.random.randn(100)
data["y"] = np.random.randn(100)
data["label"] = "other"
x=data.x.values
y=data.y.values
label=data.label.values
output_file("toolbar.html")
source = ColumnDataSource(
data=dict(
x=x,
y=y,
_class=label,
)
)
hover = HoverTool(
tooltips=[
("index", "$index"),
("(x,y)", "($x, $y)"),
("class", "#_class"),
]
)
p = figure(plot_width=400, plot_height=400, tools=[hover,"lasso_select","crosshair",],
title="Mouse over the dots")
p.circle('x', 'y', size=5, source=source)
show(p)