Pandas introduction pt2¶

In [2]:

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from scipy.optimize import curve_fit

Read excel¶

In [3]:

VP_0 = pd.read_excel('../data_example/VP_0.xls')

In [4]:

VP_0.head()

Out[4]:

	Time	Probe 1	Probe 2	Probe 3	Probe 4	Probe 5	Probe 6	Probe 7	Probe 8	Probe 9	...	Probe 12	Probe 13	Probe 14	Probe 15	Probe 16	Probe 17	Probe 18	Probe 19	Probe 20	Probe 21
0	50	289.376111	289.376111	290.575118	291.174581	291.515100	291.733607	291.898818	292.056090	292.284635	...	293.589032	293.584915	293.581635	293.583390	293.588737	293.595974	293.605148	293.611802	293.629430	293.644078
1	100	287.901449	287.901449	288.575860	289.058297	289.797472	290.798329	291.656605	292.250442	292.598401	...	292.904840	292.953306	293.004116	293.054238	293.098271	293.133411	293.160647	293.188356	293.206350	293.231139
2	150	287.948612	287.948612	288.415667	288.755772	289.031504	289.284695	289.547629	289.842949	290.188918	...	291.590653	292.061341	292.422399	292.651971	292.776545	292.842487	292.886851	292.932896	292.970032	293.016734
3	200	287.592819	287.592819	287.918203	288.210424	288.489569	288.749405	289.007951	289.305117	289.695596	...	291.425171	291.919330	292.281324	292.504363	292.618280	292.677838	292.722651	292.768211	292.816206	292.870071
4	250	286.258222	286.258222	286.987976	287.594413	287.985362	288.266699	288.499115	288.742149	289.081378	...	290.925270	291.507675	291.960919	292.261082	292.425151	292.510575	292.569019	292.619213	292.680676	292.743178

5 rows × 22 columns

to read a xls file you must install some libraries¶

conda install -c anaconda xlrd

or¶

conda install -c anaconda openpyxl

In [6]:

VP_0.describe().T

Out[6]:

	count	mean	std	min	25%	50%	75%	max
Time	100.0	2525.000000	1450.574599	50.000000	1287.500000	2525.000000	3762.500000	5000.000000
Probe 1	100.0	281.839057	1.960932	278.947433	280.836111	281.427994	282.236856	289.376111
Probe 2	100.0	281.839057	1.960932	278.947433	280.836111	281.427994	282.236856	289.376111
Probe 3	100.0	282.370682	1.927571	280.059285	281.328314	281.839689	282.541269	290.575118
Probe 4	100.0	282.826708	1.901521	280.453923	281.763463	282.192104	282.985121	291.174581
Probe 5	100.0	283.236591	1.880251	281.225892	282.223479	282.520303	283.238620	291.515100
Probe 6	100.0	283.609673	1.867328	281.859219	282.662369	282.881864	283.501267	291.733607
Probe 7	100.0	283.946638	1.858506	282.201623	283.029520	283.205617	283.900152	291.898818
Probe 8	100.0	284.255495	1.849069	282.559461	283.331851	283.472959	284.184786	292.250442
Probe 9	100.0	284.545696	1.843800	282.929516	283.609442	283.748334	284.440377	292.598401
Probe 10	100.0	284.824160	1.874454	283.285842	283.891730	284.009999	284.680521	293.081948
Probe 11	100.0	285.085605	1.908345	283.628855	284.142301	284.250969	284.921684	293.544586
Probe 12	100.0	285.334945	1.938197	283.946968	284.388941	284.486797	285.179815	293.589032
Probe 13	100.0	285.578355	1.973969	284.230365	284.618124	284.722081	285.414528	293.584915
Probe 14	100.0	285.816822	2.012527	284.568295	284.840941	284.935910	285.629832	293.581635
Probe 15	100.0	286.042042	2.055259	284.885343	285.047070	285.142718	285.814676	293.583390
Probe 16	100.0	286.247726	2.107125	285.094819	285.221555	285.315494	285.960903	293.588737
Probe 17	100.0	286.441879	2.172995	285.256668	285.353289	285.451338	286.087069	293.595974
Probe 18	100.0	286.659869	2.249566	285.388748	285.481010	285.573308	286.310354	293.605148
Probe 19	100.0	287.023858	2.287191	285.599214	285.719487	285.822354	287.048658	293.611802
Probe 20	100.0	287.878417	2.076399	286.484717	286.596061	286.710981	288.289137	293.629430
Probe 21	100.0	289.099593	1.616344	287.986741	288.066249	288.159567	289.568905	293.644078

In [7]:

VP_0.head()

Out[7]:

	Time	Probe 1	Probe 2	Probe 3	Probe 4	Probe 5	Probe 6	Probe 7	Probe 8	Probe 9	...	Probe 12	Probe 13	Probe 14	Probe 15	Probe 16	Probe 17	Probe 18	Probe 19	Probe 20	Probe 21
0	50	289.376111	289.376111	290.575118	291.174581	291.515100	291.733607	291.898818	292.056090	292.284635	...	293.589032	293.584915	293.581635	293.583390	293.588737	293.595974	293.605148	293.611802	293.629430	293.644078
1	100	287.901449	287.901449	288.575860	289.058297	289.797472	290.798329	291.656605	292.250442	292.598401	...	292.904840	292.953306	293.004116	293.054238	293.098271	293.133411	293.160647	293.188356	293.206350	293.231139
2	150	287.948612	287.948612	288.415667	288.755772	289.031504	289.284695	289.547629	289.842949	290.188918	...	291.590653	292.061341	292.422399	292.651971	292.776545	292.842487	292.886851	292.932896	292.970032	293.016734
3	200	287.592819	287.592819	287.918203	288.210424	288.489569	288.749405	289.007951	289.305117	289.695596	...	291.425171	291.919330	292.281324	292.504363	292.618280	292.677838	292.722651	292.768211	292.816206	292.870071
4	250	286.258222	286.258222	286.987976	287.594413	287.985362	288.266699	288.499115	288.742149	289.081378	...	290.925270	291.507675	291.960919	292.261082	292.425151	292.510575	292.569019	292.619213	292.680676	292.743178

5 rows × 22 columns

In [8]:

for col in VP_0.columns:
    print(col)

Time
Probe 1
Probe 2
Probe 3
Probe 4
Probe 5
Probe 6
Probe 7
Probe 8
Probe 9
Probe 10
Probe 11
Probe 12
Probe 13
Probe 14
Probe 15
Probe 16
Probe 17
Probe 18
Probe 19
Probe 20
Probe 21

In [9]:

c_list = VP_0.columns
c_list[1:]

Out[9]:

Index(['Probe 1', 'Probe 2', 'Probe 3', 'Probe 4', 'Probe 5', 'Probe 6',
       'Probe 7', 'Probe 8', 'Probe 9', 'Probe 10', 'Probe 11', 'Probe 12',
       'Probe 13', 'Probe 14', 'Probe 15', 'Probe 16', 'Probe 17', 'Probe 18',
       'Probe 19', 'Probe 20', 'Probe 21'],
      dtype='object')

In [10]:

VP_0[c_list[1:]].var()

Out[10]:

Probe 1     3.845253
Probe 2     3.845253
Probe 3     3.715531
Probe 4     3.615784
Probe 5     3.535343
Probe 6     3.486915
Probe 7     3.454044
Probe 8     3.419058
Probe 9     3.399598
Probe 10    3.513578
Probe 11    3.641781
Probe 12    3.756606
Probe 13    3.896555
Probe 14    4.050264
Probe 15    4.224088
Probe 16    4.439977
Probe 17    4.721907
Probe 18    5.060548
Probe 19    5.231241
Probe 20    4.311432
Probe 21    2.612569
dtype: float64

In [11]:

VP_0.agg(['std', 'sum', 'mean', 'median', 'var'])

Out[11]:

	Time	Probe 1	Probe 2	Probe 3	Probe 4	Probe 5	Probe 6	Probe 7	Probe 8	Probe 9	...	Probe 12	Probe 13	Probe 14	Probe 15	Probe 16	Probe 17	Probe 18	Probe 19	Probe 20	Probe 21
std	1.450575e+03	1.960932	1.960932	1.927571	1.901521	1.880251	1.867328	1.858506	1.849069	1.843800	...	1.938197	1.973969	2.012527	2.055259	2.107125	2.172995	2.249566	2.287191	2.076399	1.616344
sum	2.525000e+05	28183.905719	28183.905719	28237.068167	28282.670812	28323.659106	28360.967278	28394.663801	28425.549510	28454.569621	...	28533.494524	28557.835536	28581.682207	28604.204192	28624.772630	28644.187914	28665.986933	28702.385833	28787.841676	28909.959325
mean	2.525000e+03	281.839057	281.839057	282.370682	282.826708	283.236591	283.609673	283.946638	284.255495	284.545696	...	285.334945	285.578355	285.816822	286.042042	286.247726	286.441879	286.659869	287.023858	287.878417	289.099593
median	2.525000e+03	281.427994	281.427994	281.839689	282.192104	282.520303	282.881864	283.205617	283.472959	283.748334	...	284.486797	284.722081	284.935910	285.142718	285.315494	285.451338	285.573308	285.822354	286.710981	288.159567
var	2.104167e+06	3.845253	3.845253	3.715531	3.615784	3.535343	3.486915	3.454044	3.419058	3.399598	...	3.756606	3.896555	4.050264	4.224088	4.439977	4.721907	5.060548	5.231241	4.311432	2.612569

5 rows × 22 columns

In [36]:

fig, ax = plt.subplots(figsize=(10,10))

ax = plt.plot(VP_0.index, VP_0.iloc[:, 1:] )
fig.legend(c_list[1:], bbox_to_anchor=(.9,.12), loc="lower left")

fig.show()

C:\Users\ferra\AppData\Local\Temp/ipykernel_8908/84754593.py:7: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
  fig.show()

In [37]:

fig, ax = plt.subplots(figsize=(10,10))
#ax.set_aspect('equal')
for col in c_list[1:]:
    ax = plt.scatter(VP_0.loc[:,['Time']], VP_0[col] )
fig.legend(VP_0.columns, bbox_to_anchor=(.9,.12), loc="lower left")
fig.show()

C:\Users\ferra\AppData\Local\Temp/ipykernel_8908/1911919354.py:6: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
  fig.show()

In [15]:

boxplot = VP_0.boxplot(['Probe 1', 'Probe 2'])

In [20]:

VP_temp = VP_0.drop('Time', axis =1)

In [21]:

VP_temp

Out[21]:

	Probe 1	Probe 2	Probe 3	Probe 4	Probe 5	Probe 6	Probe 7	Probe 8	Probe 9	Probe 10	...	Probe 12	Probe 13	Probe 14	Probe 15	Probe 16	Probe 17	Probe 18	Probe 19	Probe 20	Probe 21
0	289.376111	289.376111	290.575118	291.174581	291.515100	291.733607	291.898818	292.056090	292.284635	293.081948	...	293.589032	293.584915	293.581635	293.583390	293.588737	293.595974	293.605148	293.611802	293.629430	293.644078
1	287.901449	287.901449	288.575860	289.058297	289.797472	290.798329	291.656605	292.250442	292.598401	292.769993	...	292.904840	292.953306	293.004116	293.054238	293.098271	293.133411	293.160647	293.188356	293.206350	293.231139
2	287.948612	287.948612	288.415667	288.755772	289.031504	289.284695	289.547629	289.842949	290.188918	290.600484	...	291.590653	292.061341	292.422399	292.651971	292.776545	292.842487	292.886851	292.932896	292.970032	293.016734
3	287.592819	287.592819	287.918203	288.210424	288.489569	288.749405	289.007951	289.305117	289.695596	290.221731	...	291.425171	291.919330	292.281324	292.504363	292.618280	292.677838	292.722651	292.768211	292.816206	292.870071
4	286.258222	286.258222	286.987976	287.594413	287.985362	288.266699	288.499115	288.742149	289.081378	289.603776	...	290.925270	291.507675	291.960919	292.261082	292.425151	292.510575	292.569019	292.619213	292.680676	292.743178
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	281.025624	281.025624	281.029778	281.216461	281.511131	281.859219	282.201623	282.559461	282.929516	283.285842	...	283.946968	284.230365	284.568295	284.885343	285.094819	285.256668	285.393427	285.658707	286.565653	288.063713
96	280.274595	280.274595	281.441081	282.191191	282.666607	283.001420	283.274046	283.535529	283.792279	284.041241	...	284.510920	284.732459	284.936955	285.117296	285.270841	285.404000	285.542684	285.806831	286.692762	288.102639
97	282.234974	282.234974	282.398670	282.577515	282.759164	282.954830	283.170102	283.397890	283.644723	283.905407	...	284.410728	284.642299	284.853669	285.040852	285.197568	285.326320	285.450449	285.687292	286.585022	288.052292
98	281.280343	281.280343	281.726138	282.122516	282.491944	282.823437	283.127568	283.422207	283.703699	283.947818	...	284.381430	284.590013	284.792321	284.981639	285.147491	285.284134	285.404786	285.611075	286.484717	287.986741
99	280.684473	280.684473	280.996389	281.467090	281.970243	282.459682	282.860816	283.216398	283.544736	283.842790	...	284.339665	284.555073	284.770609	284.975282	285.153536	285.298747	285.430388	285.679335	286.577299	288.053499

100 rows × 21 columns

In [25]:

fig, ax = plt.subplots(figsize=(15,10))

plt.title("Probes Temperature Boxplot\n", fontsize=25)

plt.xlabel('\nProbe number', fontsize=18)
plt.ylabel('Temperatures K°\n', fontsize=18)

plt.grid(axis='y', color='lightgrey')
plt.grid(axis='x', color='lightgrey')

ax.boxplot(VP_temp, patch_artist=True)
#ax.set_xticklabels(labels =c_list[1:],  rotation=90)
#plt.xticks(rotation=90)

# make sure you have a output folder created
plt.savefig( './output/boxplot' + '.png', format ='png',  bbox_inches='tight')
plt.show()