Develop Reference

Pandas plot a repeating dataframe issue

I am having some problems with plotting a Pandas dataframe with repeating range on x-axis after every 17 points. It doesn't start from new line after repetition. How to fix this issue.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('BS.xlsx')
plt.plot(df.BZ, df.energy)
plt.show()
Repeating Dataframe

Based on the df provided. You can try as below:
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('BS.xlsx')
df['range']= df.index//17
ax = plt.axes()
df.groupby('range').apply(lambda x:x.plot(x='BZ', y= 'energy', legend = False, ax=ax))
plt.show()

Plotting a bar chart

I have an imported excel file in python and want to create a bar chart.
In the bar chart, I want the bars to be separated by profit, 0-10, 10-20, 20-30...
How do I do this?
this is one of the things I have tried:
import NumPy as np
import matplotlib.pyplot as plt
%matplotlib inline
df.plot(kind="bar",x="profit", y="people")
df[df.profit<=10]
plt.show()
and:
df[df.profit range (10,20)]

It is a bit difficult to help you better without a sample of your data, but I constructed a dataset randomly that should have the shape of yours, so that this solution can hopefully be useful to you:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# For random data
import random
%matplotlib inline
df = pd.DataFrame({'profit':[random.choice([i for i in range(100)]) for x in range(100)], 'people':[random.choice([i for i in range(100)]) for x in range(100)]})
display(df)
out = pd.cut(df['profit'], bins=[x*10 for x in range(10)], include_lowest=True)
ax = out.value_counts(sort=False).plot.bar(rot=0, color="b", figsize=(14,4))
plt.xlabel("Profit")
plt.ylabel("People")
plt.show()
I had a look at another question on here (Pandas bar plot with binned range) and there they explained how this issue can be solved.
Hope it helps :)

How to change xticks to yearly interval in pandas time series plot

I am very new to pandas, and I have searched many StackOverflow questions similar to this for changing xtick labels yearly, but they all are different did not solve my problem, so I decided to ask my own question.
Here is my question. I have a mock data frame which I want to plot yearly xticks in the x-axis.
import numpy as np
import pandas as pd
df = pd.DataFrame({'date': pd.date_range('1991-01-01','2019-01-01')}).set_index('date')
df['value'] = np.random.randn(len(df))
df.plot()
This gives:
Xticks ==> 1995 2000 2005 etc
But I want ==> 1991 1992 ... 2019
How to do that?
So far I have tried this:
import matplotlib
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
fig,ax = plt.subplots()
df.plot(ax=ax)
ax.xaxis.set_major_locator(matplotlib.dates.YearLocator(base=1))
# ax.xaxis.set_minor_locator(matplotlib.dates.YearLocator(base=1))
# ax.set_xticklabels(list(df.index.time))
This gives just 2005 as xtick and nothing has worked till now.
Links I looked:
- Changing xticks in a pandas plot
- Python: Change the time on xticks for Pandas Plot
- https://matplotlib.org/3.1.1/api/dates_api.html

You need to use the x_compat=True argument to have pandas choose the units in a way that they are compatible with matplotlib.dates locators and formatters.
df.plot(ax=ax, x_compat=True)
Complete code:
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
df = pd.DataFrame({'date': pd.date_range('1991-01-01','2019-01-01')}).set_index('date')
df['value'] = np.random.randn(len(df))
fig,ax = plt.subplots()
df.plot(ax=ax, x_compat=True)
ax.xaxis.set_major_locator(matplotlib.dates.YearLocator(base=1))
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y"))
plt.show()

You can try this:
import datetime
# create xticks
xticks = pd.date_range(datetime.datetime(1990,1,1), datetime.datetime(2020,1,1), freq='YS')
# plot
fig, ax = plt.subplots(figsize=(12,8))
df['value'].plot(ax=ax,xticks=xticks.to_pydatetime())
ax.set_xticklabels([x.strftime('%Y') for x in xticks]);
plt.xticks(rotation=90);
Complete Example
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import datetime
# data
df = pd.DataFrame({'date': pd.date_range('1991-01-01','2019-01-01')}).set_index('date')
df['value'] = np.random.randn(len(df))
# create xticks
xticks = pd.date_range(datetime.datetime(1990,1,1), datetime.datetime(2020,1,1), freq='YS')
# plot
fig, ax = plt.subplots(figsize=(12,8))
df['value'].plot(ax=ax,xticks=xticks.to_pydatetime())
ax.set_xticklabels([x.strftime('%Y') for x in xticks]);
plt.xticks(rotation=90);
plt.show()
This gives:

How do I make one line in this graph a different color from the rest?

I have a graph, and I would like to make one of my lines different color
Tried using the matplotlib recommendation which just made me print two graphs
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv("C:\\Users\\Nathan\\Downloads\\markouts_changed_maskedNEW.csv");
data.columns = ["Yeet","Yeet1","Yeet 2","Yeet 3","Yeet 4","Yeet 7","Exchange 5","Yeet Average","Intelligent Yeet"];
mpg = data[data.columns]
mpg.plot(color='green', linewidth=2.5)

How to save multiple plot in a loop?

I tried to save multiple plot in a loop, but It draw them over each other. what should I do?
sample code:
import pandas as pd
import seaborn as sns
data=pd.DataFrame({'a':[1,2,3,4,5,6],'b':[0,1,1,0,1,1],'c':[0,0,0,1,1,1]})
for i in ['b','c']:
img=sns.boxplot(data.a, groupby=data[i])
fig = img.get_figure()
fig.savefig(i)

You need to clear the data from the previous figure which is rolling over in the loop. This should work, noting fig.clf() as the end of each loop:
import pandas as pd
import seaborn as sns
data=pd.DataFrame({'a':[1,2,3,4,5,6],'b':[0,1,1,0,1,1],'c':[0,0,0,1,1,1]})
for i in ['b','c']:
img=sns.boxplot(data.a, groupby=data[i])
fig = img.get_figure()
fig.savefig(i)
fig.clf()