Matplotlib

Subfigures Matplotlib

fig, axs = plt.subplots(n_rows,n_cols, sharex=True, sharey=True, figsize=(15,15))

for idx, face in enumerate(eigenfaces[:n_rows*n_cols]):
    axs[idx//n_rows, idx%n_cols].plot(your_data)

Plotly

Crear figura con multiples líneas

import plotly.express as px
import pandas as pd

df = pd.read_csv("data/serie_atocha_crudo.csv")
# columnas = fecha, motos_disponibles, iniciados, finalizados


fig = px.line(df, x="fecha", y=["motos_disponibles", "iniciados"],  width=2200, height=1000, title="layout.hovermode='x'")
fig.update_layout(hovermode="x") # para que al pasar el cursor te muestre ambos valores
fig.show()

Figure with multiple lines from different dfs and vetical line with annotation

import plotly.graph_objects as go
import plotly.express as px

fig1 = px.line(title="Ratings por tema en números absolutos")


fig1.add_trace(
          go.Scatter(
            x=quejas['fecha'],
            y=quejas["counts_"],
            name="name",            
            #line = dict(color=colors[i]),
            mode="lines"
          )
)


fig1.add_trace(
    go.Scatter(
        x=df_count_posneg['date_trunc'],
        y=df_count_posneg["count_negativos"],
        name="negativos (<= 3 estrellas)",            
        line = dict(color="red", width=1,dash='dash'),
        mode="lines"
        #dash="dash"
    )
)

fig1.add_trace(
    go.Scatter(
        x=df_count_posneg['date_trunc'],
        y=df_count_posneg["count_positivos"],
        name="positivos (> 3 estrellas)",            
        line = dict(color="green", width=1,dash='dash'),
        mode="lines"
        #dash="dash"
    )
)

fig1.add_annotation(x="2021-05-01", y=20000,
    text="Ratings pasan de 3 a 5 estrellas",
    showarrow=True,
    arrowhead=1
)

fig1.add_vline(x="2021-05-01", line_width=0.5)#, x1=2)

fig1.show()

Histogram when Y axis is already computed

Use px.bar()

px.histogram() won´t work because it will try to count the Y axis again lmao.

          fecha                   finalizados
0   2021-10-06 00:00:00   2
1   2021-10-07 00:00:00   39
2   2021-10-08 00:00:00   63
3   2021-10-09 00:00:00   44
4   2021-10-10 00:00:00   60

fig = px.bar(finalizados_dias_df, x="fecha", y="finalizados")
fig.show()

Folium

Create a Choropleth or Grid map with tooltip

m = folium.Map(location=[55.6829, 12.5775], zoom_start=11, tiles="cartodbpositron")

folium.Choropleth(
    geo_data=grid_with_rentals, # GeoDataFrame
    fill_color="RdPu",
    data=grid_with_rentals,
    columns=["id", "count_log"], # id of the grid, count
    key_on='feature.properties.id', # id of the grid
    line_opacity=1,
    line_weight=0.1
).add_to(m)

# Tooltips
style = {'fillColor': '#00000000', 'color': '#00000000'}

feature = folium.features.GeoJson(grid_with_rentals,
  name='count', # Column to show the data of each grid
  style_function=lambda x: style,
  tooltip=folium.GeoJsonTooltip(fields=["count"],aliases=["count"],labels=True))
m.add_child(feature)

Add elements to different layer so they can be deselected

Just create a FeatureGroup and add stuff to it

means_group = folium.FeatureGroup(name="Centroids").add_to(m)

# Adding markers to the FeatureGroup
[means_group.add_child(folium.Marker(mean, popup="Cluster center")) for mean in means]

folium.LayerControl().add_to(m)