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How to plot colors for two variables in scatterplot in python?

I have a dataset with two different variables, i want to give colors to each with different color, Can anyone help please? Link to my dataset : "https://github.com/mayuripandey/Data-Analysis/blob/main/word.csv"
import matplotlib.pyplot as plt
import pandas as pd
fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(x = df['Friends Network-metrics'], y = df['Number of Followers'],cmap = "magma")
plt.xlabel("Friends Network-metrics")
plt.ylabel("Number of Followers")
plt.show()

Not very clear what you want to do here. But I'll provide a solution that may help you a bit.
Could use seaborn to implement the colors on the variables. Otherwise, you'd need to iterate through the points to set the color. Or create a new column that conditionally inputs a color for a value.
I don't know what your variable is, but you just want to put that in for the hue parameter:
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('https://raw.githubusercontent.com/mayuripandey/Data-Analysis/main/word.csv')
# Use the 'hue' argument to provide a factor variable
sns.lmplot(x='Friends Network-metrics',
y='Number of Followers',
height=8,
aspect=.8,
data=df,
fit_reg=False,
hue='Sentiment',
legend=True)
plt.xlabel("Friends Network-metrics")
plt.ylabel("Number of Followers")
plt.show()
This can give you a view like this:
If you were looking for color scale for one of the variables though, you would do the below. However, the max value is so big that the range also doesn't make it really an effective visual:
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/mayuripandey/Data-Analysis/main/word.csv')
fig, ax = plt.subplots(figsize=(10, 6))
g = ax.scatter(x = df['Friends Network-metrics'],
y = df['Number of Followers'],
c = df['Friends Network-metrics'],
cmap = "magma")
fig.colorbar(g)
plt.xlabel("Friends Network-metrics")
plt.ylabel("Number of Followers")
plt.show()
So you could adjust the scale (I'd also add edgecolors = 'black' as its hard to see the light plots):
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/mayuripandey/Data-Analysis/main/word.csv')
fig, ax = plt.subplots(figsize=(10, 6))
g = ax.scatter(x = df['Friends Network-metrics'],
y = df['Number of Followers'],
c = df['Friends Network-metrics'],
cmap = "magma",
vmin=0, vmax=10000,
edgecolors = 'black')
fig.colorbar(g)
plt.xlabel("Friends Network-metrics")
plt.ylabel("Number of Followers")
plt.show()

Plotting seaborn histplot bar_label with condition

I want to plot a seaborn histogram with labels to show the values of each bar. I only want to show the non-zero values, but I'm not sure how to do it. My MWE is
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
xlist = 900+200*np.random.randn(50,1)
fig, ax = plt.subplots()
y = sns.histplot(data=xlist, element="bars", bins=20, stat='count', legend=False)
y.set(xlabel='total time (ms)')
y.bar_label(y.containers[0])
## y.bar_label(y.containers[0][y.containers[0]!=0])
plt.show()
The graph looks like
and I want to remove all the 0 labels.

Update
A best version suggested by #BigBen:
labels = [str(v) if v else '' for v in y.containers[0].datavalues]
y.bar_label(y.containers[0], labels=labels)
Try:
labels = []
for p in y.patches:
h = p.get_height()
labels.append(str(h) if h else '')
y.bar_label(y.containers[0], labels=labels)

How to put a colorbar in seaborn scatterplot legend

I have the next scatterplot
But i want to change the dots on the legend by continuos color map like this:
This is my code:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_style("whitegrid")
gene_list = pd.read_csv('interseccion.csv', header=None)
glist = gene_list.squeeze().str.strip().tolist()
names = gp.get_library_name()
enr = gp.enrichr(gene_list= glist,
gene_sets=['KEGG_2019_Human'],
organism='Human', # don't forget to set organism to the one you desired! e.g. Yeast
description='KEGG',
# no_plot=True,
cutoff=0.5 # test dataset, use lower value from range(0,1)
)
resultados = enr.results.head(15)
resultados['-log10(FDR)'] = -np.log10(resultados['Adjusted P-value'])
resultados['Genes'] = resultados['Genes'].str.split(';')
resultados['Genes'] = resultados['Genes'].apply(lambda x: len(x))
g = sns.scatterplot(data=resultados, x="-log10(FDR)", y="Term", hue='-log10(FDR)', palette="seismic"
, size="Genes", sizes=(30, 300), legend=True)
g.legend(loc=6, bbox_to_anchor=(1, 0.5), ncol=1)
g.fig.colorbar()
plt.ylabel('')
plt.xlabel('-log10(FDR)')
When i try to put a color bar with the funcion plt.colorbar() is not possible

I customized the code in the official sample with the understanding that I wanted to add a legend and color bars to the Seaborn scatterplot. A colormap has been created to match the colors of the sample graph, but it can be drawn without problems by specifying the colormap name. The color bar is customized by getting its position and adjusting it manually in the legend. The height of the color bar is halved to match the legend.
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
fig, ax = plt.subplots()
g = sns.scatterplot(
data=tips, x="total_bill", y="tip", hue="size", size="size",
sizes=(20, 200), legend="full", ax=ax)
g.legend(loc='upper right', bbox_to_anchor=(1.2, 1.0), ncol=1)
norm = plt.Normalize(tips['size'].min(), tips['size'].max())
cmap = sns.cubehelix_palette(light=1, as_cmap=True)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cax = fig.add_axes([ax.get_position().x1+0.05, ax.get_position().y0, 0.06, ax.get_position().height / 2])
ax.figure.colorbar(sm, cax=cax)
plt.show()

How to format seaborn/matplotlib axis tick labels from number to thousands or Millions? (125,436 to 125.4K)

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
import pandas as pd
sns.set(style="darkgrid")
fig, ax = plt.subplots(figsize=(8, 5))
palette = sns.color_palette("bright", 6)
g = sns.scatterplot(ax=ax, x="Area", y="Rent/Sqft", hue="Region", marker='o', data=df, s=100, palette= palette)
g.legend(bbox_to_anchor=(1, 1), ncol=1)
g.set(xlim = (50000,250000))
How can I can change the axis format from a number to custom format? For example, 125000 to 125.00K

IIUC you can format the xticks and set these:
In[60]:
#generate some psuedo data
df = pd.DataFrame({'num':[50000, 75000, 100000, 125000], 'Rent/Sqft':np.random.randn(4), 'Region':list('abcd')})
df
Out[60]:
num Rent/Sqft Region
0 50000 0.109196 a
1 75000 0.566553 b
2 100000 -0.274064 c
3 125000 -0.636492 d
In[61]:
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
import pandas as pd
sns.set(style="darkgrid")
fig, ax = plt.subplots(figsize=(8, 5))
palette = sns.color_palette("bright", 4)
g = sns.scatterplot(ax=ax, x="num", y="Rent/Sqft", hue="Region", marker='o', data=df, s=100, palette= palette)
g.legend(bbox_to_anchor=(1, 1), ncol=1)
g.set(xlim = (50000,250000))
xlabels = ['{:,.2f}'.format(x) + 'K' for x in g.get_xticks()/1000]
g.set_xticklabels(xlabels)
Out[61]:
The key bit here is this line:
xlabels = ['{:,.2f}'.format(x) + 'K' for x in g.get_xticks()/1000]
g.set_xticklabels(xlabels)
So this divides all the ticks by 1000 and then formats them and sets the xtick labels
UPDATE
Thanks to #ScottBoston who has suggested a better method:
ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: '{:,.2f}'.format(x/1000) + 'K'))
see the docs

The canonical way of formatting the tick labels in the standard units is to use an EngFormatter. There is also an example in the matplotlib docs.
Also see Tick locating and formatting
Here it might look as follows.
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
import pandas as pd
df = pd.DataFrame({"xaxs" : np.random.randint(50000,250000, size=20),
"yaxs" : np.random.randint(7,15, size=20),
"col" : np.random.choice(list("ABC"), size=20)})
fig, ax = plt.subplots(figsize=(8, 5))
palette = sns.color_palette("bright", 6)
sns.scatterplot(ax=ax, x="xaxs", y="yaxs", hue="col", data=df,
marker='o', s=100, palette="magma")
ax.legend(bbox_to_anchor=(1, 1), ncol=1)
ax.set(xlim = (50000,250000))
ax.xaxis.set_major_formatter(ticker.EngFormatter())
plt.show()

Using Seaborn without importing matplotlib:
import seaborn as sns
sns.set()
chart = sns.relplot(x="x_val", y="y_val", kind="line", data=my_data)
ticks = chart.axes[0][0].get_xticks()
xlabels = ['$' + '{:,.0f}'.format(x) for x in ticks]
chart.set_xticklabels(xlabels)
chart.fig
Thank you to EdChum's answer above for getting me 90% there.

Here's how I'm solving this: (similar to ScottBoston)
from matplotlib.ticker import FuncFormatter
f = lambda x, pos: f'{x/10**3:,.0f}K'
ax.xaxis.set_major_formatter(FuncFormatter(f))

We could used the APIs: ax.get_xticklabels() , get_text() and ax.set_xticklabels do it.
e.g,
xlabels = ['{:.2f}k'.format(float(x.get_text().replace('−', '-')))/1000 for x in g.get_xticklabels()]
g.set_xticklabels(xlabels)

seaborn: how to make a tsplot square

I would like to create a tsplot, where the x and the y axis are the same length. in other words the aspect ratio of the graph should be 1.
this dos not work:
fig, ax = plt.subplots()
fig.set_size_inches(2, 2)
sns.tsplot(data=df, condition=' ', time='time', value='value', unit=' ', ax=ax)

You could change the aspect ratio of your plots by controlling the aspect
parameter of a matplotlib object as shown:
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
np.random.seed(22)
sns.set_style("whitegrid")
gammas = sns.load_dataset("gammas")
fig = plt.figure()
ax = fig.add_subplot(111, aspect=2) #Use 'equal' to have the same scaling for x and y axes
sns.tsplot(time="timepoint", value="BOLD signal", unit="subject",
condition="ROI", data=gammas, ax=ax)
plt.tight_layout()
plt.show()

A little more direct is ax.set_box_aspect(1)1