Plotting series on different axes

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1

Im using this code to plot my data but because their ranges are quite different, i need different axes to view the smaller time series. Here is my code:

import pandas as pd
from pandas import Series
%matplotlib inline
from statsmodels.tsa.seasonal import seasonal_decompose
from sklearn.preprocessing import RobustScaler

# Seasonal exploration visualizing ts
import matplotlib.pyplot as plt
import seaborn as sns

# Set 3 of commands to plot 1 column from multiple series on excel file
df=pd.read_excel('DataLSTMReady.xlsx')
df=df.set_index('Date')
df=df.iloc[:,[0,1,3]] #df=df.iloc[:,0:4]
list_of_column_names = list(df.columns)
print(list_of_column_names)
print(df)

df[list_of_column_names].plot()#df['Infl Mens'].plot()

sns.set(rc={'figure.figsize':(11, 4)})
df[list_of_column_names].std()

and I get all the data as expected and this chart at the end:

Screen Shot 2022-09-24 at 8.59.58 PM
Screen Shot 2022-09-24 at 8.59.58 PM1080×724 120 KB

Here is my data:…uh, how do i attach an excel file?

2

I managed to do this I found in stackoverflow:

import pandas as pd
from pandas import Series
%matplotlib inline
from statsmodels.tsa.seasonal import seasonal_decompose
from sklearn.preprocessing import RobustScaler

# Seasonal exploration visualizing ts
import matplotlib.pyplot as plt
import seaborn as sns

#nifty = Series.from_csv('ManualStepTimeSeries.csv',header=0)
# Set 1 of commands to explore dataseries
#nifty.head()
#nifty.size
#nifty.describe()

# Set 2 of commands to plot series
# from matplotlib import pyplot
# pyplot.plot(nifty)
# pyplot.show()

# Set 3 of commands to plot 1 column from multiple series on excel file
df=pd.read_excel('DataLSTMReady.xlsx')
df=df.set_index('Date')
df=df.iloc[:,[0,1,3]] #df=df.iloc[:,0:4]
list_of_column_names = list(df.columns)
print(list_of_column_names)
print(df)

# #MATPLOTLIB PLOTS
# #FIRST EXAMPLE FULL SERIES PLOT - TURN OFF WHEN SEASONAL PLOT EXPLORATION
# #imported matplotlib as plt
ax1 = df['107057'].plot(style='b-') #plots 1 series
ax2 = ax1.twinx()
ax2.spines['right'].set_position(('axes', 1.0))
df['Infl Mens'].plot(ax=ax2,style='r-') #plots 2 series
ax3 = ax1.twinx()
ax3.spines['right'].set_position(('axes', 1.1))
df['107057-mkp'].plot(ax=ax3,style='g-') #plots 3 series

sns.set(rc={'figure.figsize':(11, 4)})

df[list_of_column_names].std()

And I get this plot which works, but Im not sure why i get those gridlines in the background.

Screen Shot 2022-09-25 at 5.17.29 PM
Screen Shot 2022-09-25 at 5.17.29 PM1104×786 162 KB

I would also like to get rid of the timestamps on the x axis and replace it with just the months. How can i format the xaxis in this combined plot to show only mm-YY? I tried this code but it doesnt change anything when I plot it. I did it for ax1, ax2 and ax3:

ax1.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))

3

dear quique123.

Thank you for your contribution to the anaconda community.

Basically, it’s a matter of technique. For example, to express as follows, use the following script.

temp2020to2022
temp2020to2022768×576 52.2 KB

fig = plt.figure(tight_layout=True, dpi=120)
plt.xlabel(“date”)
plt.ylabel(“Temp ℃”)
plt.title(‘temperature in Tokyo’)
plt.xticks(np.arange(0, cnt, step=90*24), rotation=45)

plt.plot(df1.date, df1.temp, label=“2020FY”)
plt.plot(df2.date, df2.temp, label=“2021FY”)
plt.plot(df3.date, df3.temp, label=“2022FY”)

plt.legend()
plt.grid()

StackOverFlow is a site where various users discuss how to use scripts and problems with the current program system.

In other words, it is a site where professional programmers or volunteers like me answer how to solve individual program issues by showing the source code.

Therefore, if possible, I would appreciate it if you could also learn about your own understanding of the program and how to solve it.

Best regards.

4

Hi, I managed to get it plotted. But I want to fix the x axis so i can see the months:

Here is my code but how can i upload my excel so you can see my data and test my code:

import pandas as pd
from pandas import Series
%matplotlib inline
from statsmodels.tsa.seasonal import seasonal_decompose
from sklearn.preprocessing import RobustScaler

# Seasonal exploration visualizing ts
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as mdates

# Set 3 of commands to plot 1 column from multiple series on excel file
df=pd.read_excel('DataLSTMReady.xlsx')
df.Date=pd.to_datetime(df.Date)
df=df.set_index('Date')
df=df.iloc[:,[0]] #df=df.iloc[:,0:4]

# #MATPLOTLIB PLOTS
ax1 = df['107057'].plot(style='b-') #plots 1 series
ax1.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
# ax2 = ax1.twinx()
# ax2.spines['right'].set_position(('axes', 1.0))
# ax2.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
# df['Infl Mens'].plot(ax=ax2,style='r-')
# ax3 = ax1.twinx()
# ax3.spines['right'].set_position(('axes', 1.1))
# ax3.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
# df['107057-mkp'].plot(ax=ax3,style='g-')

sns.set(rc={'figure.figsize':(11, 4)})

Here are my results but as you can see the Y-m x axis is not correct:

Screen Shot 2022-09-26 at 4.23.01 PM
Screen Shot 2022-09-26 at 4.23.01 PM922×1084 89.9 KB

Here is my excel: data

5

dear quique123.

Thanks for the detailed script and reply.

The reason is very simple. The solution can be found at the following site.

for your information.

Best regards.

6

Hi,

Thanks, I looked at the code in the stack post. This is the code I came up with but I get get nan values for all my data when I print(df) so Im creating the df incorrectly but dont know how:

import numpy
import pandas as pd
from pandas import Series
%matplotlib inline
from statsmodels.tsa.seasonal import seasonal_decompose
from sklearn.preprocessing import RobustScaler

# Seasonal exploration visualizing ts
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as mdates
df=pd.read_excel('DataLSTMReady.xlsx')

# Create DateRange
date_range = pd.date_range('1/1/2016', periods=72, freq='m')
df=pd.DataFrame(df,index=date_range)
print(df)

df=df.set_index('Date')

ax1 = df['107057'].plot(style='b-') #plots 1 series
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))

But I get all nan values for the newly created df so Im not creating my df properly:

Screen Shot 2022-09-27 at 3.04.31 PM
Screen Shot 2022-09-27 at 3.04.31 PM1352×878 66.6 KB

7

I printed the date range and it seems fine:

import numpy
import pandas as pd
from pandas import Series
%matplotlib inline
from statsmodels.tsa.seasonal import seasonal_decompose
from sklearn.preprocessing import RobustScaler

# Seasonal exploration visualizing ts
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as mdates

df=pd.read_excel('DataLSTMReady.xlsx')
date_range = pd.date_range('1/1/2016', periods=72, freq='m')
print(date_range)
#df2=pd.DataFrame(df,index=date_range)
#print(df2)

ax1 = df2['107057'].plot(style='b-') #plots 1 series
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))

This is what I get when i print out the date_range:

DatetimeIndex(['2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30',
               '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31',
               '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31',
               '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30',
               '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',
               '2017-09-30', '2017-10-31', '2017-11-30', '2017-12-31',
               '2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
               '2018-05-31', '2018-06-30', '2018-07-31', '2018-08-31',
               '2018-09-30', '2018-10-31', '2018-11-30', '2018-12-31',
               '2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',
               '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',
               '2019-09-30', '2019-10-31', '2019-11-30', '2019-12-31',
               '2020-01-31', '2020-02-29', '2020-03-31', '2020-04-30',
               '2020-05-31', '2020-06-30', '2020-07-31', '2020-08-31',
               '2020-09-30', '2020-10-31', '2020-11-30', '2020-12-31',
               '2021-01-31', '2021-02-28', '2021-03-31', '2021-04-30',
               '2021-05-31', '2021-06-30', '2021-07-31', '2021-08-31',
               '2021-09-30', '2021-10-31', '2021-11-30', '2021-12-31'],
              dtype='datetime64[ns]', freq='M')