MATPLOTLIB Archives - icodebroker

[PYTHON/PANDAS] DataFrame 클래스 : cumsum 메소드를 사용해 누적 합계 구하기

■ DataFrame 클래스의 cumsum 메소드를 사용해 누적 합계를 구하는 방법을 보여준다. ▶ main.py


import pandas            as pd
import numpy             as np
import matplotlib.pyplot as plt

datetimeIndex = pd.date_range("2000/01/01", periods = 1000)

dataFrame1 = pd.DataFrame(
    np.random.randn(1000, 4),
    index   = datetimeIndex,
    columns = ["A", "B", "C", "D"]
)

print(dataFrame1)

print()

"""
                   A         B         C         D
2000-01-01  1.499520 -0.628333 -1.435202  0.167799
2000-01-02 -0.591156 -2.497511  1.069914 -2.325316
2000-01-03 -1.073116 -1.088665  1.397093  0.932822
2000-01-04 -0.469965 -0.413086 -1.022118 -0.991445
2000-01-05  0.449823 -0.029230 -0.934880  0.413968
...              ...       ...       ...       ...
2002-09-22  3.135395 -0.130802 -0.730063  0.581774
2002-09-23 -0.627195 -0.640609  0.678296 -0.741038
2002-09-24 -0.095009 -0.509879 -0.754776 -1.355951
2002-09-25 -1.322082  0.110753 -0.686825 -0.849719
2002-09-26 -0.691853  1.422983 -2.265978 -1.229776

[1000 rows x 4 columns]
"""

dataFrame2 = dataFrame1.cumsum()

print(dataFrame2)

"""
                    A          B          C          D
2000-01-01   1.499520  -0.628333  -1.435202   0.167799
2000-01-02   0.908364  -3.125844  -0.365288  -2.157517
2000-01-03  -0.164752  -4.214509   1.031805  -1.224695
2000-01-04  -0.634718  -4.627595   0.009687  -2.216140
2000-01-05  -0.184895  -4.656825  -0.925193  -1.802172
...               ...        ...        ...        ...
2002-09-22 -60.715338 -52.075193 -26.766710  15.256986
2002-09-23 -61.342533 -52.715801 -26.088414  14.515948
2002-09-24 -61.437541 -53.225681 -26.843190  13.159998
2002-09-25 -62.759624 -53.114928 -27.530015  12.310278
2002-09-26 -63.451477 -51.691945 -29.795993  11.080502

[1000 rows x 4 columns]
"""

dataFrame2.plot()

plt.show()

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

datetimeIndex = pd.date_range("2000/01/01", periods = 1000)

dataFrame1 = pd.DataFrame(

np.random.randn(1000, 4),

index = datetimeIndex,

columns = ["A", "B", "C", "D"]

)

print(dataFrame1)

print()

"""

A B C D

2000-01-01 1.499520 -0.628333 -1.435202 0.167799

2000-01-02 -0.591156 -2.497511 1.069914 -2.325316

2000-01-03 -1.073116 -1.088665 1.397093 0.932822

2000-01-04 -0.469965 -0.413086 -1.022118 -0.991445

2000-01-05 0.449823 -0.029230 -0.934880 0.413968

... ... ... ... ...

2002-09-22 3.135395 -0.130802 -0.730063 0.581774

2002-09-23 -0.627195 -0.640609 0.678296 -0.741038

2002-09-24 -0.095009 -0.509879 -0.754776 -1.355951

2002-09-25 -1.322082 0.110753 -0.686825 -0.849719

2002-09-26 -0.691853 1.422983 -2.265978 -1.229776

[1000 rows x 4 columns]

"""

dataFrame2 = dataFrame1.cumsum()

print(dataFrame2)

"""

A B C D

2000-01-01 1.499520 -0.628333 -1.435202 0.167799

2000-01-02 0.908364 -3.125844 -0.365288 -2.157517

2000-01-03 -0.164752 -4.214509 1.031805 -1.224695

2000-01-04 -0.634718 -4.627595 0.009687 -2.216140

2000-01-05 -0.184895 -4.656825 -0.925193 -1.802172

... ... ... ... ...

2002-09-22 -60.715338 -52.075193 -26.766710 15.256986

2002-09-23 -61.342533 -52.715801 -26.088414 14.515948

2002-09-24 -61.437541 -53.225681 -26.843190 13.159998

2002-09-25 -62.759624 -53.114928 -27.530015 12.310278

2002-09-26 -63.451477 -51.691945 -29.795993 11.080502

[1000 rows x 4 columns]

"""

dataFrame2.plot()

plt.show()

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.3
packaging==24.2
pandas==2.2.3
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.3

packaging==24.2

pandas==2.2.3

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas matplotlib

[PYTHON/PANDAS] Series 클래스 : cumsum 메소드를 사용해 누적 합계 구하기

■ Series 클래스의 cumsum 메소드를 사용해 누적 합계를 구하는 방법을 보여준다. ▶ main.py


import pandas            as pd
import numpy             as np
import matplotlib.pyplot as plt

datetimeIndex = pd.date_range("2000/01/01", periods = 1000)

series1 = pd.Series(np.random.randn(1000), index = datetimeIndex)

print(series1)

"""
2000-01-01    1.605094
2000-01-02    1.032125
2000-01-03    2.041472
2000-01-04    1.341634
2000-01-05   -0.410063
                ...
2002-09-22   -1.272603
2002-09-23    1.260618
2002-09-24   -0.654690
2002-09-25    0.085590
2002-09-26    0.936237
Freq: D, Length: 1000, dtype: float64
"""

print()

series2 = series1.cumsum()

print(series2)

"""
2000-01-01     1.605094
2000-01-02     2.637219
2000-01-03     4.678692
2000-01-04     6.020325
2000-01-05     5.610263
                ...
2002-09-22    17.052849
2002-09-23    18.313467
2002-09-24    17.658777
2002-09-25    17.744367
2002-09-26    18.680604
Freq: D, Length: 1000, dtype: float64
"""

series2.plot()

plt.show()

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

datetimeIndex = pd.date_range("2000/01/01", periods = 1000)

series1 = pd.Series(np.random.randn(1000), index = datetimeIndex)

print(series1)

"""

2000-01-01 1.605094

2000-01-02 1.032125

2000-01-03 2.041472

2000-01-04 1.341634

2000-01-05 -0.410063

...

2002-09-22 -1.272603

2002-09-23 1.260618

2002-09-24 -0.654690

2002-09-25 0.085590

2002-09-26 0.936237

Freq: D, Length: 1000, dtype: float64

"""

print()

series2 = series1.cumsum()

print(series2)

"""

2000-01-01 1.605094

2000-01-02 2.637219

2000-01-03 4.678692

2000-01-04 6.020325

2000-01-05 5.610263

...

2002-09-22 17.052849

2002-09-23 18.313467

2002-09-24 17.658777

2002-09-25 17.744367

2002-09-26 18.680604

Freq: D, Length: 1000, dtype: float64

"""

series2.plot()

plt.show()

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.3
packaging==24.2
pandas==2.2.3
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.3

packaging==24.2

pandas==2.2.3

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas matplotlib

[PYTHON/MATPLOTLIB] close 함수 : 모든 윈도우 닫기

■ close 함수를 사용해 모든 윈도우를 닫는 방법을 보여준다. ▶ main.py


import matplotlib.pyplot as plt

plt.close("all")

import matplotlib.pyplot as plt

plt.close("all")

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.3
packaging==24.2
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
six==1.16.0

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.3

packaging==24.2

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

six==1.16.0

※ pip install matplotlib 명령을 실행했다.

[PYTHON/PANDAS] DataFrame 클래스 : plot 속성을 사용해 누적 AREA 차트 그리기

■ DataFrame 클래스의 plot 속성을 사용해 누적 AREA 차트를 그리는 방법을 보여준다. ▶ main.py


import pandas            as pd
import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

figure, axes = plt.subplots(figsize = (12, 4))

plotAccessor.area(ax = axes)

axes.set_ylabel("NO$_2$ concentration")

figure.savefig("no2_concentrations.png")

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

figure, axes = plt.subplots(figsize = (12, 4))

plotAccessor.area(ax = axes)

axes.set_ylabel("NO$_2$ concentration")

figure.savefig("no2_concentrations.png")

plt.show()

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.2
packaging==24.1
pandas==2.2.3
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.2

packaging==24.1

pandas==2.2.3

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas

[PYTHON/PANDAS] DataFrame 클래스 : plot 속성을 사용해 AREA 차트 그리기

■ DataFrame 클래스의 plot 속성을 사용해 AREA 차트를 그리는 방법을 보여준다. ▶ main.py


import pandas            as pd
import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

plotAccessor.area(figsize = (12, 4), subplots = True)

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

plotAccessor.area(figsize = (12, 4), subplots = True)

plt.show()

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.2
packaging==24.1
pandas==2.2.3
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.2

packaging==24.1

pandas==2.2.3

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas matplotlib

[PYTHON/PANDAS] DataFrame 클래스 : plot 속성을 사용해 BOX-PLOT 차트 그리기

■ DataFrame 클래스의 plot 속성을 사용해 BOX-PLOT 차트를 그리는 방법을 보여준다. ▶ main.py


import pandas            as pd
import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

plotAccessor.box()

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

plotAccessor.box()

plt.show()

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.2
packaging==24.1
pandas==2.2.3
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.2

packaging==24.1

pandas==2.2.3

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas matplotlib

[PYTHON/PANDAS] dir 함수 : PlotAccessor 클래스 메소드 출력하기

■ dir 함수를 사용해 PlotAccessor 클래스의 메소드를 출력하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

methodNameList = [methodName for methodName in dir(plotAccessor) if not methodName.startswith("_")]

for methodName in methodNameList:
    print(methodName)

"""
area
bar
barh
box
density
hexbin
hist
kde
line
pie
scatter
"""

import pandas as pd

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

methodNameList = [methodName for methodName in dir(plotAccessor) if not methodName.startswith("_")]

for methodName in methodNameList:

print(methodName)

"""

area

bar

barh

box

density

hexbin

hist

kde

line

pie

scatter

"""

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.2
packaging==24.1
pandas==2.2.3
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.2

packaging==24.1

pandas==2.2.3

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas matplotlib 명령을

[PYTHON/PANDAS] DataFrame 클래스 : plot 속성을 사용해 SCATTER 차트 그리기

■ DataFrame 클래스의 plot 속성을 사용해 SCATTER 차트를 그리는 방법을 보여준다. ▶ main.py


import pandas            as pd
import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

plotAccessor.scatter(x = "station_london", y = "station_paris", alpha = 0.5)

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

plotAccessor = dataFrame.plot

plotAccessor.scatter(x = "station_london", y = "station_paris", alpha = 0.5)

plt.show()

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.2
packaging==24.1
pandas==2.2.3
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.2

packaging==24.1

pandas==2.2.3

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas matplotlib

[PYTHON/PANDAS] Series 클래스 : plot 메소드를 사용해 LINE 차트 그리기

■ Series 클래스의 plot 메소드를 사용해 LINE 차트를 그리는 방법을 보여준다. ▶ main.py


import pandas            as pd
import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

series = dataFrame["station_paris"]

series.plot()

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

series = dataFrame["station_paris"]

series.plot()

plt.show()

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.2
packaging==24.1
pandas==2.2.3
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.2

packaging==24.1

pandas==2.2.3

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas matplotlib

[PYTHON/PANDAS] DataFrame 클래스 : plot 메소드를 사용해 LINE 차트 그리기

■ DataFrame 클래스의 plot 메소드를 사용해 LINE 차트를 그리는 방법을 보여준다. ▶ main.py


import pandas            as pd
import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

dataFrame.plot()

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True)

dataFrame.plot()

plt.show()

▶ requirements.txt


contourpy==1.3.0
cycler==0.12.1
fonttools==4.54.1
kiwisolver==1.4.7
matplotlib==3.9.2
numpy==2.1.2
packaging==24.1
pandas==2.2.3
pillow==11.0.0
pyparsing==3.2.0
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

contourpy==1.3.0

cycler==0.12.1

fonttools==4.54.1

kiwisolver==1.4.7

matplotlib==3.9.2

numpy==2.1.2

packaging==24.1

pandas==2.2.3

pillow==11.0.0

pyparsing==3.2.0

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas matplotlib

[PYTHON/MATPLOTLIB] plot 함수 : Scatter 차트 그리기

■ plot 함수를 사용해 Scatter 차트를 그리는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np

trainXNDArray = np.random.random((1000, 1))
trainYNDArray = trainXNDArray * 2 + np.random.random((1000, 1)) / 3.0

testXNDArray = np.random.random((100, 1))
testYNDArray = testXNDArray * 2 + np.random.random((100, 1)) / 3.0

pp.plot(trainXNDArray, trainYNDArray, "ro")
pp.plot(testXNDArray , testYNDArray , "bo")

pp.legend(["training", "test"], loc = "upper left")

pp.show()

import matplotlib.pyplot as pp

import numpy as np

trainXNDArray = np.random.random((1000, 1))

trainYNDArray = trainXNDArray * 2 + np.random.random((1000, 1)) / 3.0

testXNDArray = np.random.random((100, 1))

testYNDArray = testXNDArray * 2 + np.random.random((100, 1)) / 3.0

pp.plot(trainXNDArray, trainYNDArray, "ro")

pp.plot(testXNDArray , testYNDArray , "bo")

pp.legend(["training", "test"], loc = "upper left")

pp.show()

[PYTHON/MATPLOTLIB] Precision-Recall Graph 그리기

■ Precision-Recall Graph를 그리는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np

modelAPrecisionNDArray  = np.array([0.33, 0.38, 0.45, 0.55, 0.57, 0.40, 0.66, 1.00, 1.00, 1.00, 1.00])
modelARecallRateNDArray = np.array([1.00, 1.00, 1.00, 1.00, 0.80, 0.40, 0.40, 0.40, 0.40, 0.20, 0.00])

modelBPrecisionNDArray  = np.array([0.33, 0.38, 0.36, 0.37, 0.33, 0.40, 0.33, 0.50, 1.00, 1.00, 1.00])
modelBRecallRateNDArray = np.array([1.00, 1.00, 0.80, 0.60, 0.40, 0.40, 0.20, 0.20, 0.20, 0.20, 0.00])

pp.title("Precision-Recall Graph")

pp.xlabel("Recall"   )
pp.ylabel("Precision")

pp.plot(modelARecallRateNDArray, modelAPrecisionNDArray, "b", label = "Model A")
pp.plot(modelBRecallRateNDArray, modelBPrecisionNDArray, "g", label = "Model B")

pp.legend(loc = "upper right")

pp.show()

import matplotlib.pyplot as pp

import numpy as np

modelAPrecisionNDArray = np.array([0.33, 0.38, 0.45, 0.55, 0.57, 0.40, 0.66, 1.00, 1.00, 1.00, 1.00])

modelARecallRateNDArray = np.array([1.00, 1.00, 1.00, 1.00, 0.80, 0.40, 0.40, 0.40, 0.40, 0.20, 0.00])

modelBPrecisionNDArray = np.array([0.33, 0.38, 0.36, 0.37, 0.33, 0.40, 0.33, 0.50, 1.00, 1.00, 1.00])

modelBRecallRateNDArray = np.array([1.00, 1.00, 0.80, 0.60, 0.40, 0.40, 0.20, 0.20, 0.20, 0.20, 0.00])

pp.title("Precision-Recall Graph")

pp.xlabel("Recall" )

pp.ylabel("Precision")

pp.plot(modelARecallRateNDArray, modelAPrecisionNDArray, "b", label = "Model A")

pp.plot(modelBRecallRateNDArray, modelBPrecisionNDArray, "g", label = "Model B")

pp.legend(loc = "upper right")

pp.show()

[PYTHON/MATPLOTLIB] ROC(Receiver Operating Characteristic) Curve 그리기

■ ROC(Receiver Operating Characteristic) Curve를 그리는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np

modelASensitivityNDArray = np.array([1.00, 1.00, 1.00, 1.00, 0.75, 0.50, 0.50, 0.50, 0.50, 0.50, 0.00])
modelASpecificityNDArray = np.array([0.00, 0.16, 0.50, 0.66, 0.66, 0.66, 0.83, 1.00, 1.00, 1.00, 1.00])
modelBSensitivityNDArray = np.array([1.00, 1.00, 0.75, 0.75, 0.50, 0.50, 0.50, 0.50, 0.25, 0.25, 0.00])
modelBSpecificityNDArray = np.array([0.00, 0.33, 0.33, 0.50, 0.50, 0.66, 0.66, 0.83, 0.83, 1.00, 1.00])

pp.title("Receiver Operating Characteristic")

pp.xlabel("False Positive Rate(1 - Specificity)")
pp.ylabel("True Positive Rate(Sensitivity)")

pp.plot(1 - modelASpecificityNDArray, modelASensitivityNDArray, "b", label = "Model A")
pp.plot(1 - modelBSpecificityNDArray, modelBSensitivityNDArray, "g", label = "Model B")

pp.plot([0, 1], [1, 1], "y--")
pp.plot([0, 1], [0, 1], "r--")

pp.legend(loc = "lower right")

pp.show()

import matplotlib.pyplot as pp

import numpy as np

modelASensitivityNDArray = np.array([1.00, 1.00, 1.00, 1.00, 0.75, 0.50, 0.50, 0.50, 0.50, 0.50, 0.00])

modelASpecificityNDArray = np.array([0.00, 0.16, 0.50, 0.66, 0.66, 0.66, 0.83, 1.00, 1.00, 1.00, 1.00])

modelBSensitivityNDArray = np.array([1.00, 1.00, 0.75, 0.75, 0.50, 0.50, 0.50, 0.50, 0.25, 0.25, 0.00])

modelBSpecificityNDArray = np.array([0.00, 0.33, 0.33, 0.50, 0.50, 0.66, 0.66, 0.83, 0.83, 1.00, 1.00])

pp.title("Receiver Operating Characteristic")

pp.xlabel("False Positive Rate(1 - Specificity)")

pp.ylabel("True Positive Rate(Sensitivity)")

pp.plot(1 - modelASpecificityNDArray, modelASensitivityNDArray, "b", label = "Model A")

pp.plot(1 - modelBSpecificityNDArray, modelBSensitivityNDArray, "g", label = "Model B")

pp.plot([0, 1], [1, 1], "y--")

pp.plot([0, 1], [0, 1], "r--")

pp.legend(loc = "lower right")

pp.show()

[PYTHON/MATPLOTLIB] savefig 함수를 사용해 SVG 파일 저장하기

■ savefig 함수를 사용해 SVG 파일을 저장하는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np

pp.figure(figsize = [6, 6])

x = np.arange(0, 100, 0.00001)
y = x * np.sin(2 * np.pi * x)

pp.plot(y)

pp.axis("off")

pp.gca().set_position([0, 0, 1, 1])

pp.savefig("test.svg")

import matplotlib.pyplot as pp

import numpy as np

pp.figure(figsize = [6, 6])

x = np.arange(0, 100, 0.00001)

y = x * np.sin(2 * np.pi * x)

pp.plot(y)

pp.axis("off")

pp.gca().set_position([0, 0, 1, 1])

pp.savefig("test.svg")

[PYTHON/MATPLOTLIB] scatter 함수 : 산점도 차트 그리기

■ scatter 함수를 사용해 산점도 차트를 그리는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np
import pandas as pd

xNDArray = np.random.normal(1, 1, size = (100, 1))
yNDArray = np.random.normal(-2, 4, size = (100, 1))
zNDArray = np.concatenate((xNDArray, yNDArray), axis = 1)

dataFrame = pd.DataFrame(zNDArray, columns = ["X", "Y"])

pp.scatter(dataFrame["X"], dataFrame["Y"])

pp.show()

import matplotlib.pyplot as pp

import numpy as np

import pandas as pd

xNDArray = np.random.normal(1, 1, size = (100, 1))

yNDArray = np.random.normal(-2, 4, size = (100, 1))

zNDArray = np.concatenate((xNDArray, yNDArray), axis = 1)

dataFrame = pd.DataFrame(zNDArray, columns = ["X", "Y"])

pp.scatter(dataFrame["X"], dataFrame["Y"])

pp.show()

[PYTHON/MATPLOTLIB] Figure 클래스 : savefig 메소드를 사용해 차트 이미지 저장하기

■ Figure 클래스의 savefig 메소드를 사용해 차트 이미지를 저장하는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np

pointCount = 1000
xList = []
yList = []

for i in range(pointCount):
    x = np.random.normal(0.0, 0.55)
    y = x * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
    xList.append(x)
    yList.append(y)

pp.plot(xList, yList, "ro")

figure = pp.gcf()

pp.grid()
pp.show()

figure.savefig("result.png")
figure.savefig("result.jpg")

import matplotlib.pyplot as pp

import numpy as np

pointCount = 1000

xList = []

yList = []

for i in range(pointCount):

x = np.random.normal(0.0, 0.55)

y = x * 0.1 + 0.3 + np.random.normal(0.0, 0.03)

xList.append(x)

yList.append(y)

pp.plot(xList, yList, "ro")

figure = pp.gcf()

pp.grid()

pp.show()

figure.savefig("result.png")

figure.savefig("result.jpg")

[PYTHON/MATPLOTLIB] plot 함수 : Scatter 차트 그리기

■ plot 함수를 사용해 Scatter 차트를 그리는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np

pointCount = 1000
xList = []
yList = []

for i in range(pointCount):
    x = np.random.normal(0.0, 0.55)
    y = x * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
    xList.append(x)
    yList.append(y)

pp.plot(xList, yList, "ro")
pp.grid()
pp.show()

import matplotlib.pyplot as pp

import numpy as np

pointCount = 1000

xList = []

yList = []

for i in range(pointCount):

x = np.random.normal(0.0, 0.55)

y = x * 0.1 + 0.3 + np.random.normal(0.0, 0.03)

xList.append(x)

yList.append(y)

pp.plot(xList, yList, "ro")

pp.grid()

pp.show()

[PYTHON/MATPLOTLIB] plot 함수 : 1차 방정식 그리기

■ plot 함수를 사용해 1차 방정식을 그리는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np

xNDArray      = np.arange(0, 10, 0.01)
yNDArray      = 3 * xNDArray + 5
yNoiseNDArray = yNDArray + np.random.randn(len(yNDArray))

pp.figure(figsize = (12, 8))

pp.plot(xNDArray, yNoiseNDArray, label = "noise", color = "y")
pp.plot(xNDArray, yNDArray, ls = "dashed", lw = 3, color = "b", label = "original")

pp.show();

import matplotlib.pyplot as pp

import numpy as np

xNDArray = np.arange(0, 10, 0.01)

yNDArray = 3 * xNDArray + 5

yNoiseNDArray = yNDArray + np.random.randn(len(yNDArray))

pp.figure(figsize = (12, 8))

pp.plot(xNDArray, yNoiseNDArray, label = "noise", color = "y")

pp.plot(xNDArray, yNDArray, ls = "dashed", lw = 3, color = "b", label = "original")

pp.show();

[PYTHON/MATPLOTLIB] plot 함수 : 1차 방정식 그리기

■ plot 함수를 사용해 1차 방정식을 그리는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp
import numpy as np

xNDArray = np.arange(0, 10, 0.01)
yNDArray = 3 * xNDArray + 5

pp.figure(figsize = (12, 8))

pp.plot(xNDArray, yNDArray, ls = "dashed", lw = 3, color = "b", label = "original")

pp.show();

import matplotlib.pyplot as pp

import numpy as np

xNDArray = np.arange(0, 10, 0.01)

yNDArray = 3 * xNDArray + 5

pp.figure(figsize = (12, 8))

pp.plot(xNDArray, yNDArray, ls = "dashed", lw = 3, color = "b", label = "original")

pp.show();

[PYTHON/MATPLOTLIB] hist 함수 : 히스토그램 출력하기

■ hist 함수를 사용해 히스토그램을 출력하는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.pyplot as pp

list = [1, 3, 2, 5, 4, 3, 7, 8, 5, 3, 2, 4, 6, 7, 8, 8]

pp.hist(list)

pp.show()

import matplotlib.pyplot as pp

list = [1, 3, 2, 5, 4, 3, 7, 8, 5, 3, 2, 4, 6, 7, 8, 8]

pp.hist(list)

pp.show()

[PYTHON/MATPLOTLIB] imshow 함수 : 이미지 출력하기

■ imshow 함수를 사용해 이미지를 출력하는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.image as img
import matplotlib.pyplot as pp

fileName = "sample.png"

ndarray = img.imread(fileName)

pp.imshow(ndarray)

pp.show()

import matplotlib.image as img

import matplotlib.pyplot as pp

fileName = "sample.png"

ndarray = img.imread(fileName)

pp.imshow(ndarray)

pp.show()

[PYTHON/MATPLOTLIB] imread 함수 : 이미지 파일 읽기

■ imread 함수를 사용해 이미지 파일을 읽는 방법을 보여준다. ▶ 예제 코드 (PY)


import matplotlib.image as img

fileName = "c:\\sample.jpg"

ndarray = img.imread(fileName)

print(type(ndarray))
print(ndarray.ndim)
print(ndarray.shape)

"""
<class 'numpy.ndarray'>
3
(1280, 1920, 3)
"""

import matplotlib.image as img

fileName = "c:\\sample.jpg"

ndarray = img.imread(fileName)

print(type(ndarray))

print(ndarray.ndim)

print(ndarray.shape)

"""

(1280, 1920, 3)

"""