I have used 4 machine learning models on a task and now I am struggling to plot their bar charts just like shown below in the image. I am printing classification report to get precision, recall etc. My code is shown: def Statistics(data): # Classification Report print("Classification Report is shown below") print(classification_report(data['actual labels'],data['predicted labels'])) # Confusion matrix print("Confusion matrix is shown below") cm=confusion_matrix(data['actual labels'],data['predicted labels']) plt.figure(figsize=(10,7)) sn.heatmap(cm, annot=True,cmap='Blues', fmt='g') plt.xlabel('Predicted') plt.ylabel('Truth') Statistics(data) How can I plot this type of chart …
I would like to plot a landscape spanned by six variables. The numerical target variable is explained by five numerical variables. Ultimately, it is about to get a visual impression for optima and the parameter landscape itself. Any advice how to proceed? I would prefer R or Python but I am open to alternatives.
I'm using Plotly's graphic_objects.Histogram. I am trying to draw several histograms on the same plot. I'm not satisfied with the results of the 'overlay' option. I would like to draw histograms with no fill colour like in this example, with only the borders of the boxes drawn. I was trying to fiddle with "marker.pattern" options but this doesn't seem to work. What would be the way to accomplish this, if this is possible?
I am making an animated choropleth map of regions in Czechia. When I run it without the animation, purely on one set of the data it takes 7.5 seconds. Here is the code for that. However when I tried making the animation I had to stop it after 16 hours without a solution. And the HTML file I was saving it in got almost to 1GB in size. Here is the code I use for the animation. fig = px.choropleth(df_anim, …
this is a more general question asking about Python libraries that are able to showcase networks with following criterias: Different size of the nodes depending on a metric (e.g. sum of some variable X received by all other nodes) Direction of the connection between the nodes (e.g. Node A sends X units to Node B, while Node B sends Y units to Node A and Q units to Node C) - So a node can receive and send at the …
For example using some code like this one: fig.add_scatter(x=x, y=y, mode='lines', line=dict(dash='dot')) The possibilities I know of are 'lines', 'dash', 'dot' and 'dashdot'. I was looking for another way to differentiate a new plot using this option, like "spaced dots" for example. Unfortunately I couldn't find anything where I looked (basically web searches and plotly line chart documentation). Is there any other possible value for this argument or is that all there is?
I am unable to figure out why below code doesn't give proper o/p as maps, but just legends. *Summary_Table is my data_frame that contains 'id' and 'Total Deaths' and geojson_data contains the location data. fig=px.choropleth(Summary_Table, locations='id', geojson=geojson_data.to_json(), color="Total Deaths" ) fig.show()
I'm plotting a dataset with a trendline, but using ols vs lowess gives me significantly different line shapes. I'm sure this is an expected and perfectly normal result, but I'm afraid I don't understand the significance of the difference. Could someone explain what's going on? Here's my code using ols: fig = px.scatter(df_small, y="Ratio", trendline="ols", trendline_color_override="red") fig.show() And here's the plot it produced: Using lowess gave me this plot: For context, my dataset represents the ratio of daily Covid deaths:new …
My date format for my axis labels works fine if the chart type is heatmap or contour, but when I make a surface plot, my date format is not taken, and a default format is used instead. I want the date format to show the month and day, leaving out the year. This is because the year goes on the y-axis, while the calendar day goes on the x-axis. I am using the year 2000 as a template for the …
kmeans = KMeans(n_clusters=4) model = kmeans.fit(europe_july) pred = model.labels_ europe_july['cluster'] = pred pca = PCA(n_components=2) pca_model = pca.fit_transform(europe_july) data_transform = pd.DataFrame(data = pca_model, columns = ['PCA1', 'PCA2']) data_transform['Cluster'] = pred plt.figure(figsize=(8,8)) g = sns.scatterplot(data=data_transform, x='PCA1', y='PCA2',\ palette=sns.color_palette()[:4], hue='Cluster') title = plt.title('World countries clusters with PCA') But when I run this code it does not seem to take into account this model. europe_july['country'] = countries europe_july['iso_alpha'] = iso_alpha fig = px.choropleth(data_frame = europe_july, locations= "iso_alpha", scope= 'world', title='2020-11-07 (World)', color= "cluster", …
I copied the "Indexing by GeoJSON Properties" example as seen on the Choropleth Maps in Python page. The code was executed with no error but there was no map showing, only the legend. In addition, the 'box', 'pan', 'zoom' options show but don't work. I have the latest plotly version, installed via pip. any ideas? Thanks.
I’m trying to show a heatmap of a large 2D array (160x250000 entries). This should go into a dash app so I'm using plotly to deal with graphics and my idea was to use datashader for performance but I’m having troubles getting it right. However, independently of dash I’m already having problem with plotly + datashader (see code below). There is probably something very basic I’m not understanding in this process. It would be great if someone could tell me …
I want to add a condition to the column where attractionName.value_counts() >=165. How can I add a query/filter/condition to a DataFrame before I plot the data. fig = px.bar(rwgor, x="attractionName", y="attraction score summary", color="attraction score summary", title="Long-Form Input") fig.show() With the above code I can plot all values in the column attractionName. I only want to plot values whose value_counts() is grater than 165.
not sure if this is correct medium, but i am having trouble plotting this simple data: how do i get the graph below - aplogies for this poor picture. basically i want it stacked with the risk label being the legend, but the predicted and actual side by side for each year.. how can i achieve this in pandas? btw this is a pandas dataframe e.g. df_partial = df[['year','risk','predicted','actual']] grouped = df_partial.groupby(['year','risk']).sum()[['predicted','actual']] grouped.plot(kind='bar') it does not give me what i …
I am trying to plot a week stock data from alpha vantage with plotly but I get weird plots compared to other source's plot like google's chart for example. data, meta_data = ts.get_intraday(symbol='MSFT',interval='1min', outputsize='full') data = data.sort_index() If I plot without the x axis labels, I get a correct plot but the x labels are replaced with integer ranges. close_graph = go.Scatter(y=data['4. close'], hoverinfo=['y']) close_layout = go.Layout(title='Intraday Times Series for the MSFT stock (1 min)') close_fig = go.Figure(data=close_graph, layout=close_layout) else …
Guys i have this table in dash the following columns are drop down columns Reason Code, Staging and Overwrite. Based on their values i want to create values under Final Staging column dynamically in dash Here is my code app.layout = html.Div([ dash_table.DataTable( id='table-dropdown', data=staging.to_dict('records'), columns=[ {'id': 'customer_id', 'name': 'Customer ID'}, {'id': 'booking_date', 'name': 'Booking Date'}, {'id': 'oustanding_balance_(currency)', 'name': 'Outstanding Balance(currency)'}, {'id': 'booking_date', 'name': 'Booking Date'}, {'id': 'past_due_days', 'name': 'Past Due Days'}, {'id': 'segment', 'name': 'Segment'}, {'id': 'contract_rate_%', 'name': 'Contract …