Develop Reference

Can I take a table from excel and plot a histogram in python?

I have 2 tables a 10 by 110 and a 35 by 110 and both contain random numbers from a exponential distribution function provided by my professor. The assignment is to prove the central limit theorem in statistics.
What I thought to try is:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"importing data"
df1 = pd.read_excel(r'C:\Users\Henry\Desktop\n10.xlsx')
df2 = pd.read_excel(r'C:\Users\Henry\Desktop\n30.xlsx')
df1avg = pd.read_excel(r'C:\Users\Henry\Desktop\n10avg.xlsx')
df2avg = pd.read_excel(r'C:\Users\Henry\Desktop\n30avg.xlsx')
"plotting n10 histogram"
plt.hist(df1, bins=34)
plt.hist(df1avg, bins=11)
"plotting n30 histogram"
plt.hist(df2, bins=63)
plt.hist(df2avg, bins=11)
Is that ok or do I need to format the tables into a singular column, and if so what is the most efficient way to do that?

I suspect that you will want to flatten your dataframe first, as illustrated below.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
N = np.random.exponential(1, [40, 5])
df = pd.DataFrame(N) # convert to dataframe
bin_edges = np.linspace(0,6,30)
plt.figure()
plt.hist(df, bins = bin_edges, density = True)
plt.xlabel('Value')
plt.ylabel('Probability density')
The multiple (5) colours of lines per bin shows the histograms for each column of the data frame.
Fortunately, this is not hard to adjust. You can convert the data frame to a numpy array and flatten it:
plt.hist(df.to_numpy().flatten(), bins = bin_edges, density = True)
plt.ylabel('Probability density')
plt.xlabel('Value')

How do I find covariance and correlation?

I have 2 data sets saved in the csv file. Column names "avg" and "hu". I want to find the covariance and correlation values ​​of these two data sets. I tried it with some simple codes. But every time I got an error. What am I doing wrong ?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv("80hucov.csv")
avg=data["avg"]
hu=data["hu"]
data = np.array(["avg, hu"])
covMatrix = np.cov(data,bias=True)
print (covMatrix)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv("80hucov.csv")
data = {'A': ["avg"],
'B': ["hu"],}
df = pd.DataFrame(data,columns=['A','B'])
covMatrix = pd.DataFrame.cov(df)
sn.heatmap(covMatrix, annot=True, fmt='g')
plt.show()

It seems you may need to redefine your definition of the array.
Currently you have:
data = np.array(["avg, hu"])
You can do:
data_array = data[['avg', 'hu']].to_numpy()
I recommend using different names for different objets within your code. In your example you use "data" for both your dataframe and your array.

Grouped Histogram in Python

Is there a simple way of creating histograms for a continuous variable (mpg) that is filtered by a categorical variable (cyl=4,8)? So essentially I need two histograms for mpg grouped by cyl, one for cyl=4 and one for cyl=8.
Here is an example from a different dataset:

import numpy as np
import pandas as pd
import seaborn as sns
data = pd.DataFrame()
data[4] = np.random.normal(0,10,300)
data[8] = np.random.normal(20,11,300)
sns.distplot(data[4], color="skyblue")
sns.distplot(data[8], color="orange")
I just used my random sample.
I am just being a little lazy here, but all you need to do is a seaborn package.
There are much more options you can handle, so please read it more here [https://python-graph-gallery.com/]

Taking data from specific columns in a dataset

I need to take data from only 3 columns in my dataset, how do I do this? I am trying to make a correlation graph. This is my code:
import matplotlib.pyplot as plt
import pandas as pd
crimedata = pd.read_csv('MasterFileCSV.csv')
crime_df = pd.DataFrame(crimedata)
plt.matshow(crime_df.corr())
plt.show

Seaborn violin plot over time given numpy ndarray

I have a distribution that changes over time for which I would like to plot a violin plot for each time step side-by-side using seaborn. My initial attempt failed as violinplot cannot handle a np.ndarray for the y argument:
import numpy as np
import seaborn as sns
time = np.arange(0, 10)
samples = np.random.randn(10, 200)
ax = sns.violinplot(x=time, y=samples) # Exception: Data must be 1-dimensional
The seaborn documentation has an example for a vertical violinplot grouped by a categorical variable. However, it uses a DataFrame in long format.
Do I need to convert my time series into a DataFrame as well? If so, how do I achieve this?

A closer look at the documentation made me realize that omitting the x and y argument altogether leads to the data argument being interpreted in wide-form:
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
samples = np.random.randn(20, 10)
ax = sns.violinplot(data=samples)
plt.show()

In the violin plot documentation it says that the input x and y parameters do not have to be a data frame, but they have a restriction of having the same dimension. In addition, the variable y that you created has 10 rows and 200 columns. This is detrimental when plotting the graphics and causes a dimension problem.
I tested it and this code has no problems when reading the python file.
import numpy as np
import seaborn as sns
import pandas as pd
time = np.arange(0, 200)
samples = np.random.randn(10, 200)
for sample in samples:
ax = sns.violinplot(x=time, y=sample)
You can then group the resulting graphs using this link:
https://python-graph-gallery.com/199-matplotlib-style-sheets/
If you want to convert your data into data frames it is also possible. You just need to use pandas.
example
import pandas as pd
x = [1,2,3,4]
df = pd.DataFrame(x)