I have a data frame (loading from CSV) file that looks like below one
Data Mean sd time__1 time__2 time__3 time__4 time__5
0 Data_1 0.947667 0.025263 0.501517 0.874750 0.929426 0.953847 0.958375
1 Data_2 0.031960 0.017314 0.377588 0.069185 0.037523 0.024028 0.021532
Now, I wanted to plot 2 time series plots for (data_1
, data_2
) with (time__1
, time__2
, etc) as a timepoint. The x axis
is (time__1
, time__2
, etc) and the y axis
is their associated values.
The code I am trying
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv("file.csv", delimiter=',', header=0)
data = data.drop(["Unnamed: 0"], axis=1)
# Set the date column as the index
data = data.set_index(["time__1", "time__2", "time__3", "time__4", "time__5"])
ax = data.plot(linewidth=2, fontsize=12)
ax.set_xlabel('Data')
ax.legend(fontsize=12)
plt.savefig("series.png")
plt.show()
The figure I am getting is not as expected.
I think I am doing some wrong with set_index()
as my time points are in different columns.
How can I plot time-series when time points are in different columns?
Reproducible data as dictionary formate
{'Data': {(0.501517236232758, 0.874750375747681, 0.929425954818726, 0.953846752643585, 0.958374977111816): 'Data_1', (0.377588421106338, 0.069185301661491, 0.037522859871388, 0.0240284409374, 0.021532088518143): 'Data_2'}, 'Mean': {(0.501517236232758, 0.874750375747681, 0.929425954818726, 0.953846752643585, 0.958374977111816): 0.947667360305786, (0.377588421106338, 0.069185301661491, 0.037522859871388, 0.0240284409374, 0.021532088518143): 0.031959813088179}, 'sd': {(0.501517236232758, 0.874750375747681, 0.929425954818726, 0.953846752643585, 0.958374977111816): 0.025263005867601, (0.377588421106338, 0.069185301661491, 0.037522859871388, 0.0240284409374, 0.021532088518143): 0.017313838005066}}
source https://stackoverflow.com/questions/71904380/how-to-plot-multiple-time-series-from-a-csv-while-the-data-points-are-in-differe
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