Develop Reference

Why is my MatPlotLib split axes graph not appearing?

I tried to create a graph side by side using matplotlib.
I don't get any errors when I run my code, instead, I just get a blank window from MatPlotLib.
Here's the link I used for my CSV.
https://ca.finance.yahoo.com/quote/%5EGSPTSE/history?p=%5EGSPTSE
Previously, I have also created a graph that overlayed the two lines(which works as intended), but they are not displaying as seperate graphs, which is what I am trying to do with my current code.
I tried this video for information in creating these graphs, but I can't replicate the graph shown in the video even when I copy the code.
https://www.youtube.com/watch?v=-2AMr95nUDw
from matplotlib import pyplot as mpl
import pandas as pd
data_better = pd.read_csv('What.csv')
# print(data_better.head()) #I used this part to find out what the headers were for x values
# print(data_better.columns[::])
mpl.axes([15000, 17000, 20000, 23000])
mpl.title("Open Values")
mpl.plot(data_better["Date"], data_better["Open"])
mpl.ylabel("Money")
mpl.axes([15000, 17000, 20000, 23000])
mpl.title("Close Values")
mpl.plot(data_better["Date"], data_better["Close"])
mpl.ylabel("Money")
mpl.show()

pyplot.axes accepts 4-tuple of floats in normalized (0, 1) units to place the axes. You can look at examples in Make Room For Ylabel Using Axesgrid to learn using it.
If you want to plot two plots in one figure, you need use different axes
from matplotlib import pyplot as plt
import pandas as pd
data_better = pd.read_csv('What.csv')
figure, (axes1, axes2) = plt.subplots(nrows=1, ncols=2)
axes1.set_title("Open Values")
axes1.plot(data_better["Date"], data_better["Open"])
axes1.set_ylabel("Money")
axes2.set_title("Close Values")
axes2.plot(data_better["Date"], data_better["Close"])
axes2.set_ylabel("Money")
plt.show()

Display all x values of a graph

I know it has already been asked, but I could not solve my problem.
I have three pandas column, One with dates, and other with values.
I can get my graph with the two curves depending on date.
However, I cannot display all dates in the x axis. Can you help me?
import pandas as pd
import matplotlib.pyplot as plt
# mau_file is the pandas dataframe with three columns.
plt.figure()
mau_file.plot(x='month_date', y=['mau', 'nb_migs'], figsize=(10,5), grid=True)
plt.set_xticklabels(mau_file['month_date'])
plt.legend(loc='best')
plt.show()

Usually, plt.xticks() is used to display x axis values.
As I'm not sure it is 100% compatible with a pandas structure, you may need to store your data in a classical table or a numpy array.
Documentation of plt.xticks()
EDIT : It is possible to chose the orientation of the labels.
For exemple plt.xticks(x, labels, rotation='vertical') will give you vertical labels.

Histogram of a dataframe

I have a data frame as follow:
and I am trying to plot a histogram from it such that the letters {A,B,C,D} are in the x axis and y axis shows the numbers. I have tried the following:
df.plot(kind='hist')
for which I get the address instead of the plot, i.e:
<matplotlib.axes._subplots.AxesSubplot at 0x11217d5f8>
I was wondering how can I show the plot?

IIUC, I think you need to transpose the dataframe to get index ['A','B','C','D']as x-axis and then plot. Also use plt.show() to display the histogram. The latest version of pandas will display directly the plot with axes object displaying. But, for the older versions need to explicitly write the plt.show() code to display.
import matplotlib.pyplot as plt
df.T.plot(kind='hist')
plt.show()

y-axis scaling in seaborn vs pandas

I'm plotting a scatter plot using a pandas dataframe. This works correctly, but I wanted to use seaborn themes and specials functions. When I plot the same data points calling seaborn, the y-axis remains almost invisible. X-axis values ranges from 5000-15000, while y-axis values are in [-6:6]*10^-7.
If I multiply the y-axis values by 10^6, they display correctly, but the actual values when plotted using seaborn remains invisible/indistinguishable in a seaborn generated plot.
How can I seaborn so that the y-axis values scale automatically in the resultant plot?
Also some rows even contain NaN, not in this case, how to disregard that while plotting, short of manually weeding out rows containing NaN.
Below is the code I've used to plot.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("datascale.csv")
subdf = df.loc[(df.types == "easy") & (df.weight > 1300), ]
subdf = subdf.iloc[1:61, ]
subdf.drop(subdf.index[[25]], inplace=True) #row containing NaN
subdf.plot(x='length', y='speed', style='s') #scales y-axis correctly
sns.lmplot("length", "speed", data=subdf, fit_reg=True, lowess=True) #doesn't scale y-axis properly
# multiplying by 10^6 displays the plot correctly, in matplotlib
plt.scatter(subdf['length'], 10**6*subdf['speed'])

Strange that seaborn does not scale the axis correctly. Nonetheless, you can correct this behaviour. First, get a reference to the axis object of the plot:
lm = sns.lmplot("length", "speed", data=subdf, fit_reg=True)
After that you can manually set the y-axis limits:
lm.axes[0,0].set_ylim(min(subdf.speed), max(subdf.speed))
The result should look something like this:
Example Jupyter notebook here.

Seaborn and matplotlib should just ignore NaN values when plotting. You should be able to leave them as is.
As for the y scaling: there might be a bug in seaborn.
The most basic workaround is still to scale the data before plotting.
Scale to microspeed in the dataframe before plotting and plot microspeed instead.
subdf['microspeed']=subdf['speed']*10**6
Or transform to log y before plotting, i.e.
import math
df = pd.DataFrame({'speed':[1, 100, 10**-6]})
df['logspeed'] = df['speed'].map(lambda x: math.log(x,10))
then plot logspeed instead of speed.
Another approach would be to use seaborn regplot instead.
Matplot lib correctly scales and plots for me as follows:
plt.plot(subdf['length'], subdf['speed'], 'o')

Barchart with vertical labels in python/matplotlib

I'm using matplotlib to generate a (vertical) barchart. The problem is my labels are rather long. Is there any way to display them vertically, either in the bar or above it or below it?

Do you mean something like this:
>>> from matplotlib import *
>>> plot(xrange(10))
>>> yticks(xrange(10), rotation='vertical')
?
In general, to show any text in matplotlib with a vertical orientation, you can add the keyword rotation='vertical'.
For further options, you can look at help(matplotlib.pyplot.text)
The yticks function plots the ticks on the y axis; I am not sure whether you originally meant this or the ylabel function, but the procedure is alwasy the same, you have to add rotation='vertical'
Maybe you can also find useful the options 'verticalalignment' and 'horizontalalignment', which allows you to define how to align the text with respect to the ticks or the other elements.

In Jupyter Notebook you might use something like this
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
plt.xticks(rotation='vertical')
plt.plot(np.random.randn(100).cumsum())
or you can use:
plt.xticks(rotation=90)

Please check out this link:
https://python-graph-gallery.com/7-custom-barplot-layout/
import matplotlib.pyplot as plt
heights = [10, 20, 15]
bars = ['A_long', 'B_long', 'C_long']
y_pos = range(len(bars))
plt.bar(y_pos, heights)
# Rotation of the bars names
plt.xticks(y_pos, bars, rotation=90)
The result will be like this
Hopefully, it helps.

I would suggest looking at the matplotlib gallery. At least two of the examples seem to be relevant:
text_rotation.py for understanding how text layout works
barchart_demo2.py, an example of a bar chart with somewhat more complicated layout than the most basic example.