10월 2024 - 8 중 2 번째 페이지

[PYTHON/PANDAS] DataFrame 클래스 : index 속성에서 DatetimeIndex 객체 구하기

■ DataFrame 클래스의 index 속성에서 DatetimeIndex 객체를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

datetimeIndexResampler = pivotDataFrame.resample("ME")

targetDataFrame = datetimeIndexResampler.max()

datetimeIndex = targetDataFrame.index

print(datetimeIndex.freq)

"""
<MonthEnd>
"""

import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

datetimeIndexResampler = pivotDataFrame.resample("ME")

targetDataFrame = datetimeIndexResampler.max()

datetimeIndex = targetDataFrame.index

print(datetimeIndex.freq)

"""

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas 명령을 실행했다.

[PYTHON/PANDAS] DatetimeIndexResampler 클래스 : max 메소드를 사용해 시계열 특정 구간의 최대값 구하기

■ DatetimeIndexResampler 클래스의 max 메소드를 사용해 시계열 특정 구간의 최대값을 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

datetimeIndexResampler = pivotDataFrame.resample("ME")

targetDataFrame = datetimeIndexResampler.max()

print(targetDataFrame)

"""
location                   BETR801  FR04014  London Westminster
datetime
2019-05-31 00:00:00+00:00     74.5     97.0                97.0
2019-06-30 00:00:00+00:00     52.5     84.7                52.0
"""

import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

datetimeIndexResampler = pivotDataFrame.resample("ME")

targetDataFrame = datetimeIndexResampler.max()

print(targetDataFrame)

"""

location BETR801 FR04014 London Westminster

datetime

2019-05-31 00:00:00+00:00 74.5 97.0 97.0

2019-06-30 00:00:00+00:00 52.5 84.7 52.0

"""

※ DatetimeIndexResampler 클래스에는 max 메소드 외에

[PYTHON/PANDAS] DataFrame 클래스 : resample 메소드를 사용해 시계열에서 다른 주기로 리샘플링하는 datetimeIndexResampler 객체 만들기

■ DataFrame 클래스의 resample 메소드를 사용해 시계열에서 다른 주기로 리샘플링하는 datetimeIndexResampler 객체를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

datetimeIndexResampler = pivotDataFrame.resample("ME")

import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

datetimeIndexResampler = pivotDataFrame.resample("ME")

※ DataFrame 클래스의 resample

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

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


import pandas            as pd
import matplotlib.pyplot as plt

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

pivotDataFrame["2019-05-20":"2019-05-21"].plot();

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

pivotDataFrame["2019-05-20":"2019-05-21"].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 : pivot 메소드에서 index/columns/values 인자를 사용해 LONG 포맷 데이터에서 WIDE 포맷 데이터 구하기

■ DataFrame 클래스의 pivot 메소드에서 index/columns/values 인자를 사용해 LONG 포맷 데이터에서 WIDE 포맷 데이터를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

"""
location                   BETR801  FR04014  London Westminster
datetime
2019-05-07 01:00:00+00:00     50.5     25.0                23.0
2019-05-07 02:00:00+00:00     45.0     27.7                19.0
2019-05-07 03:00:00+00:00      NaN     50.4                19.0
2019-05-07 04:00:00+00:00      NaN     61.9                16.0
2019-05-07 05:00:00+00:00      NaN     72.4                 NaN
...                            ...      ...                 ...
2019-06-20 20:00:00+00:00      NaN     21.4                 NaN
2019-06-20 21:00:00+00:00      NaN     24.9                 NaN
2019-06-20 22:00:00+00:00      NaN     26.5                 NaN
2019-06-20 23:00:00+00:00      NaN     21.8                 NaN
2019-06-21 00:00:00+00:00      NaN     20.0                 NaN

[1033 rows x 3 columns]
"""

import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

pivotDataFrame = renameDataFrame.pivot(index = "datetime", columns = "location", values = "value")

"""

location BETR801 FR04014 London Westminster

datetime

2019-05-07 01:00:00+00:00 50.5 25.0 23.0

2019-05-07 02:00:00+00:00 45.0 27.7 19.0

2019-05-07 03:00:00+00:00 NaN 50.4 19.0

2019-05-07 04:00:00+00:00 NaN 61.9 16.0

2019-05-07 05:00:00+00:00 NaN 72.4 NaN

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

2019-06-20 20:00:00+00:00 NaN 21.4 NaN

2019-06-20 21:00:00+00:00 NaN 24.9 NaN

2019-06-20 22:00:00+00:00 NaN 26.5 NaN

2019-06-20 23:00:00+00:00 NaN 21.8 NaN

2019-06-21 00:00:00+00:00 NaN 20.0 NaN

[1033 rows x 3 columns]

"""

▶ requirements.txt

[PYTHON/PANDAS] DataFrame 클래스 : 시계열 데이터를 사용해 BAR 차트 그리기

■ DataFrame 클래스에서 시계열 데이터를 사용해 BAR 차트를 그리는 방법을 보여준다. ▶ main.py


import pandas            as pd
import matplotlib.pyplot as plt

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

targetDataFrameGroupBy = renameDataFrame.groupby([renameDataFrame["datetime"].dt.hour])

targetSeriesGroupBy = targetDataFrameGroupBy["value"]

targetSeries = targetSeriesGroupBy.mean()

figure, axes = plt.subplots(figsize = (12, 4)) # 가로 12인치 세로 4인치

targetSeries.plot(kind = "bar", rot = 0, ax = axes) # 막대 그래프, X축 레이블 회전 0도

plt.xlabel("Hour of the day");
plt.ylabel("$NO_2 (µg/m^3)$");

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

targetDataFrameGroupBy = renameDataFrame.groupby([renameDataFrame["datetime"].dt.hour])

targetSeriesGroupBy = targetDataFrameGroupBy["value"]

targetSeries = targetSeriesGroupBy.mean()

figure, axes = plt.subplots(figsize = (12, 4)) # 가로 12인치 세로 4인치

targetSeries.plot(kind = "bar", rot = 0, ax = axes) # 막대 그래프, X축 레이블 회전 0도

plt.xlabel("Hour of the day");

plt.ylabel("$NO_2 (µg/m^3)$");

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 air_quality_no2_long.csv

[PYTHON/PANDAS] DataFrame 클래스 : groupby 메소드를 사용해 특정 컬럼들 그룹별 평균값 구하기

■ DataFrame 클래스의 groupby 메소드를 사용해 특정 컬럼들의 그룹별 평균값을 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

targetDataFrameGroupBy = renameDataFrame.groupby([renameDataFrame["datetime"].dt.weekday, "location"])

targetSeriesGroupBy = targetDataFrameGroupBy["value"]

targetSeries = targetSeriesGroupBy.mean()

print(targetSeries)

"""
datetime  location
0         BETR801               27.875000
          FR04014               24.856250
          London Westminster    23.969697
1         BETR801               22.214286
          FR04014               30.999359
          London Westminster    24.885714
2         BETR801               21.125000
          FR04014               29.165753
          London Westminster    23.460432
3         BETR801               27.500000
          FR04014               28.600690
          London Westminster    24.780142
4         BETR801               28.400000
          FR04014               31.617986
          London Westminster    26.446809
5         BETR801               33.500000
          FR04014               25.266154
          London Westminster    24.977612
6         BETR801               21.896552
          FR04014               23.274306
          London Westminster    24.859155
Name: value, dtype: float64
"""

import pandas as pd

sourceDataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameDataFrame = sourceDataFrame.rename(columns = {"date.utc" : "datetime"})

targetDataFrameGroupBy = renameDataFrame.groupby([renameDataFrame["datetime"].dt.weekday, "location"])

targetSeriesGroupBy = targetDataFrameGroupBy["value"]

targetSeries = targetSeriesGroupBy.mean()

print(targetSeries)

"""

datetime location

0 BETR801 27.875000

FR04014 24.856250

London Westminster 23.969697

1 BETR801 22.214286

FR04014 30.999359

London Westminster 24.885714

2 BETR801 21.125000

FR04014 29.165753

London Westminster 23.460432

3 BETR801 27.500000

FR04014 28.600690

London Westminster 24.780142

4 BETR801 28.400000

FR04014 31.617986

London Westminster 26.446809

5 BETR801 33.500000

FR04014 25.266154

London Westminster 24.977612

6 BETR801 21.896552

FR04014 23.274306

London Westminster 24.859155

Name: value, dtype: float64

"""

※ renameDataFrame[“datetime”].dt.weekday 속성에서 0은 월요일, 6은

[PYTHON/PANDAS] Series 클래스 : dt 속성을 사용해 DataFrame 객체에 월별 컬럼 추가하기

■ Series 클래스의 dt 속성을 사용해 DataFrame 객체에 월별 컬럼을 추가하는 방법을 보여준다. ▶ main.py


import pandas            as pd
import matplotlib.pyplot as plt

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

datetimeSeries = renameAirQualityNO2DataFrame["datetime"]

renameAirQualityNO2DataFrame["month"] = datetimeSeries.dt.month

print(renameAirQualityNO2DataFrame)

"""
        city country                  datetime            location parameter  value   unit  month
0      Paris      FR 2019-06-21 00:00:00+00:00             FR04014       no2   20.0  µg/m³      6
1      Paris      FR 2019-06-20 23:00:00+00:00             FR04014       no2   21.8  µg/m³      6
2      Paris      FR 2019-06-20 22:00:00+00:00             FR04014       no2   26.5  µg/m³      6
3      Paris      FR 2019-06-20 21:00:00+00:00             FR04014       no2   24.9  µg/m³      6
4      Paris      FR 2019-06-20 20:00:00+00:00             FR04014       no2   21.4  µg/m³      6
...      ...     ...                       ...                 ...       ...    ...    ...    ...
2063  London      GB 2019-05-07 06:00:00+00:00  London Westminster       no2   26.0  µg/m³      5
2064  London      GB 2019-05-07 04:00:00+00:00  London Westminster       no2   16.0  µg/m³      5
2065  London      GB 2019-05-07 03:00:00+00:00  London Westminster       no2   19.0  µg/m³      5
2066  London      GB 2019-05-07 02:00:00+00:00  London Westminster       no2   19.0  µg/m³      5
2067  London      GB 2019-05-07 01:00:00+00:00  London Westminster       no2   23.0  µg/m³      5

[2068 rows x 8 columns]
"""

import pandas as pd

import matplotlib.pyplot as plt

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

datetimeSeries = renameAirQualityNO2DataFrame["datetime"]

renameAirQualityNO2DataFrame["month"] = datetimeSeries.dt.month

print(renameAirQualityNO2DataFrame)

"""

city country datetime location parameter value unit month

0 Paris FR 2019-06-21 00:00:00+00:00 FR04014 no2 20.0 µg/m³ 6

1 Paris FR 2019-06-20 23:00:00+00:00 FR04014 no2 21.8 µg/m³ 6

2 Paris FR 2019-06-20 22:00:00+00:00 FR04014 no2 26.5 µg/m³ 6

3 Paris FR 2019-06-20 21:00:00+00:00 FR04014 no2 24.9 µg/m³ 6

4 Paris FR 2019-06-20 20:00:00+00:00 FR04014 no2 21.4 µg/m³ 6

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

2063 London GB 2019-05-07 06:00:00+00:00 London Westminster no2 26.0 µg/m³ 5

2064 London GB 2019-05-07 04:00:00+00:00 London Westminster no2 16.0 µg/m³ 5

2065 London GB 2019-05-07 03:00:00+00:00 London Westminster no2 19.0 µg/m³ 5

2066 London GB 2019-05-07 02:00:00+00:00 London Westminster no2 19.0 µg/m³ 5

2067 London GB 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 µg/m³ 5

[2068 rows x 8 columns]

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install

[PYTHON/PANDAS] read_csv 함수 : parse_dates 인자를 사용해 특정 컬럼을 날짜 타입으로 로드하기

■ read_csv 함수의 parse_dates 인자를 사용해 특정 컬럼을 날짜 타입으로 로드하는 방법을 보여준다. ▶ main.py


import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

print(renameAirQualityNO2DataFrame["datetime"].dtype) # datetime64[ns, UTC]

import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv", parse_dates = ["date.utc"])

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

print(renameAirQualityNO2DataFrame["datetime"].dtype) # datetime64[ns, UTC]

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install

[PYTHON/PANDAS] to_datetime 함수 : DataFrame 객체의 특정 컬럼을 날짜 타입으로 변경하기

■ to_datetime 함수를 사용해 DataFrame 객체의 특정 컬럼을 날짜 타입으로 변경하는 방법을 보여준다. ▶ main.py


import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv")

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

print(renameAirQualityNO2DataFrame["datetime"].dtype) # object

renameAirQualityNO2DataFrame["datetime"] = pd.to_datetime(renameAirQualityNO2DataFrame["datetime"])

print(renameAirQualityNO2DataFrame["datetime"].dtype) # datetime64[ns, UTC]

import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv")

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

print(renameAirQualityNO2DataFrame["datetime"].dtype) # object

renameAirQualityNO2DataFrame["datetime"] = pd.to_datetime(renameAirQualityNO2DataFrame["datetime"])

print(renameAirQualityNO2DataFrame["datetime"].dtype) # datetime64[ns, UTC]

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install

[PYTHON/PANDAS] Series 클래스 : unique 메소드를 사용해 유일 값 리스트 구하기

■ Series 클래스의 unique 메소드를 사용해 유일 값 리스트를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv")

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

citySeries = renameAirQualityNO2DataFrame.city

cityNDArray = citySeries.unique()

print(cityNDArray)

"""
['Paris' 'Antwerpen' 'London']
"""

import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv")

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

citySeries = renameAirQualityNO2DataFrame.city

cityNDArray = citySeries.unique()

print(cityNDArray)

"""

['Paris' 'Antwerpen' 'London']

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas

[PYTHON/PANDAS] DataFrame 클래스 : 특정 컬럼 Series 객체 구하기

■ DataFrame 클래스에서 특정 컬럼 Series 객체를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv")

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

citySeries = renameAirQualityNO2DataFrame.city

import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv")

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

citySeries = renameAirQualityNO2DataFrame.city

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas 명령을 실행했다.

[PYTHON/PANDAS] DataFrame 클래스 : rename 메소드에서 columns 인자를 사용해 컬럼명 변경하기

■ DataFrame 클래스의 rename 메소드에서 columns 인자를 사용해 컬럼명을 변경하는 방법을 보여준다. ▶ main.py


import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv")

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

print(renameAirQualityNO2DataFrame)

"""
        city country                   datetime            location parameter  value   unit
0      Paris      FR  2019-06-21 00:00:00+00:00             FR04014       no2   20.0  µg/m³
1      Paris      FR  2019-06-20 23:00:00+00:00             FR04014       no2   21.8  µg/m³
2      Paris      FR  2019-06-20 22:00:00+00:00             FR04014       no2   26.5  µg/m³
3      Paris      FR  2019-06-20 21:00:00+00:00             FR04014       no2   24.9  µg/m³
4      Paris      FR  2019-06-20 20:00:00+00:00             FR04014       no2   21.4  µg/m³
...      ...     ...                        ...                 ...       ...    ...    ...
2063  London      GB  2019-05-07 06:00:00+00:00  London Westminster       no2   26.0  µg/m³
2064  London      GB  2019-05-07 04:00:00+00:00  London Westminster       no2   16.0  µg/m³
2065  London      GB  2019-05-07 03:00:00+00:00  London Westminster       no2   19.0  µg/m³
2066  London      GB  2019-05-07 02:00:00+00:00  London Westminster       no2   19.0  µg/m³
2067  London      GB  2019-05-07 01:00:00+00:00  London Westminster       no2   23.0  µg/m³

[2068 rows x 7 columns]
"""

import pandas as pd

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv")

renameAirQualityNO2DataFrame = airQualityNO2DataFrame.rename(columns = {"date.utc" : "datetime"})

print(renameAirQualityNO2DataFrame)

"""

city country datetime location parameter value unit

0 Paris FR 2019-06-21 00:00:00+00:00 FR04014 no2 20.0 µg/m³

1 Paris FR 2019-06-20 23:00:00+00:00 FR04014 no2 21.8 µg/m³

2 Paris FR 2019-06-20 22:00:00+00:00 FR04014 no2 26.5 µg/m³

3 Paris FR 2019-06-20 21:00:00+00:00 FR04014 no2 24.9 µg/m³

4 Paris FR 2019-06-20 20:00:00+00:00 FR04014 no2 21.4 µg/m³

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

2063 London GB 2019-05-07 06:00:00+00:00 London Westminster no2 26.0 µg/m³

2064 London GB 2019-05-07 04:00:00+00:00 London Westminster no2 16.0 µg/m³

2065 London GB 2019-05-07 03:00:00+00:00 London Westminster no2 19.0 µg/m³

2066 London GB 2019-05-07 02:00:00+00:00 London Westminster no2 19.0 µg/m³

2067 London GB 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 µg/m³

[2068 rows x 7 columns]

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas

[PYTHON/PANDAS] merge 함수 : how/left_on/right_on 인자를 사용해 데이터 조인하기

■ merge 함수의 how/left_on/right_on 인자를 사용해 데이터를 조인하는 방법을 보여준다. ▶ main.py


import pandas as pd

airQualityParameterDataFrame = pd.read_csv("air_quality_parameters.csv")

airQualityNO2DataFrame  = pd.read_csv("air_quality_no2_long.csv" , parse_dates = True)[["date.utc", "location", "parameter", "value"]]
AirQualityPM25DataFrame = pd.read_csv("air_quality_pm25_long.csv", parse_dates = True)[["date.utc", "location", "parameter", "value"]]

airQualityDataFrame = pd.concat([AirQualityPM25DataFrame, airQualityNO2DataFrame], axis = 0).sort_values("date.utc")

targetDataFrame = pd.merge(airQualityDataFrame, airQualityParameterDataFrame, how = "left", left_on = "parameter", right_on = "id")

print(targetDataFrame)

"""
                       date.utc            location parameter  value    id                                        description   name
0     2019-05-07 01:00:00+00:00  London Westminster       no2   23.0   no2                                   Nitrogen Dioxide    NO2
1     2019-05-07 01:00:00+00:00             FR04014       no2   25.0   no2                                   Nitrogen Dioxide    NO2
2     2019-05-07 01:00:00+00:00             BETR801      pm25   12.5  pm25  Particulate matter less than 2.5 micrometers i...  PM2.5
3     2019-05-07 01:00:00+00:00             BETR801       no2   50.5   no2                                   Nitrogen Dioxide    NO2
4     2019-05-07 01:00:00+00:00  London Westminster      pm25    8.0  pm25  Particulate matter less than 2.5 micrometers i...  PM2.5
...                         ...                 ...       ...    ...   ...                                                ...    ...
3173  2019-06-20 22:00:00+00:00             FR04014       no2   26.5   no2                                   Nitrogen Dioxide    NO2
3174  2019-06-20 23:00:00+00:00  London Westminster      pm25    7.0  pm25  Particulate matter less than 2.5 micrometers i...  PM2.5
3175  2019-06-20 23:00:00+00:00             FR04014       no2   21.8   no2                                   Nitrogen Dioxide    NO2
3176  2019-06-21 00:00:00+00:00  London Westminster      pm25    7.0  pm25  Particulate matter less than 2.5 micrometers i...  PM2.5
3177  2019-06-21 00:00:00+00:00             FR04014       no2   20.0   no2                                   Nitrogen Dioxide    NO2

[3178 rows x 7 columns]
"""

import pandas as pd

airQualityParameterDataFrame = pd.read_csv("air_quality_parameters.csv")

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv" , parse_dates = True)[["date.utc", "location", "parameter", "value"]]

AirQualityPM25DataFrame = pd.read_csv("air_quality_pm25_long.csv", parse_dates = True)[["date.utc", "location", "parameter", "value"]]

airQualityDataFrame = pd.concat([AirQualityPM25DataFrame, airQualityNO2DataFrame], axis = 0).sort_values("date.utc")

targetDataFrame = pd.merge(airQualityDataFrame, airQualityParameterDataFrame, how = "left", left_on = "parameter", right_on = "id")

print(targetDataFrame)

"""

date.utc location parameter value id description name

0 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 no2 Nitrogen Dioxide NO2

1 2019-05-07 01:00:00+00:00 FR04014 no2 25.0 no2 Nitrogen Dioxide NO2

2 2019-05-07 01:00:00+00:00 BETR801 pm25 12.5 pm25 Particulate matter less than 2.5 micrometers i... PM2.5

3 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 no2 Nitrogen Dioxide NO2

4 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 pm25 Particulate matter less than 2.5 micrometers i... PM2.5

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

3173 2019-06-20 22:00:00+00:00 FR04014 no2 26.5 no2 Nitrogen Dioxide NO2

3174 2019-06-20 23:00:00+00:00 London Westminster pm25 7.0 pm25 Particulate matter less than 2.5 micrometers i... PM2.5

3175 2019-06-20 23:00:00+00:00 FR04014 no2 21.8 no2 Nitrogen Dioxide NO2

3176 2019-06-21 00:00:00+00:00 London Westminster pm25 7.0 pm25 Particulate matter less than 2.5 micrometers i... PM2.5

3177 2019-06-21 00:00:00+00:00 FR04014 no2 20.0 no2 Nitrogen Dioxide NO2

[3178 rows x 7 columns]

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas 명령을 실행했다.

[PYTHON/PANDAS] merge 함수 : how/on 인자를 사용해 데이터 조인하기

■ merge 함수의 how/on 인자를 사용해 데이터를 조인하는 방법을 보여준다. ▶ main.py


import pandas as pd

airQualityStationDataFrame = pd.read_csv("air_quality_stations.csv"  )
airQualityNO2DataFrame     = pd.read_csv("air_quality_no2_long.csv" , parse_dates = True)[["date.utc", "location", "parameter", "value"]]
AirQualityPM25DataFrame    = pd.read_csv("air_quality_pm25_long.csv", parse_dates = True)[["date.utc", "location", "parameter", "value"]]

airQualityDataFrame = pd.concat([AirQualityPM25DataFrame, airQualityNO2DataFrame], axis = 0).sort_values("date.utc")

targetDataFrame = pd.merge(airQualityDataFrame, airQualityStationDataFrame, how = "left", on = "location")

print(targetDataFrame)

"""
                       date.utc            location parameter  value  coordinates.latitude  coordinates.longitude
0     2019-05-07 01:00:00+00:00  London Westminster       no2   23.0              51.49467               -0.13193
1     2019-05-07 01:00:00+00:00             FR04014       no2   25.0              48.83724                2.39390
2     2019-05-07 01:00:00+00:00             FR04014       no2   25.0              48.83722                2.39390
3     2019-05-07 01:00:00+00:00             BETR801      pm25   12.5              51.20966                4.43182
4     2019-05-07 01:00:00+00:00             BETR801       no2   50.5              51.20966                4.43182
...                         ...                 ...       ...    ...                   ...                    ...
4177  2019-06-20 23:00:00+00:00             FR04014       no2   21.8              48.83724                2.39390
4178  2019-06-20 23:00:00+00:00             FR04014       no2   21.8              48.83722                2.39390
4179  2019-06-21 00:00:00+00:00  London Westminster      pm25    7.0              51.49467               -0.13193
4180  2019-06-21 00:00:00+00:00             FR04014       no2   20.0              48.83724                2.39390
4181  2019-06-21 00:00:00+00:00             FR04014       no2   20.0              48.83722                2.39390

[4182 rows x 6 columns]
"""

import pandas as pd

airQualityStationDataFrame = pd.read_csv("air_quality_stations.csv" )

airQualityNO2DataFrame = pd.read_csv("air_quality_no2_long.csv" , parse_dates = True)[["date.utc", "location", "parameter", "value"]]

AirQualityPM25DataFrame = pd.read_csv("air_quality_pm25_long.csv", parse_dates = True)[["date.utc", "location", "parameter", "value"]]

airQualityDataFrame = pd.concat([AirQualityPM25DataFrame, airQualityNO2DataFrame], axis = 0).sort_values("date.utc")

targetDataFrame = pd.merge(airQualityDataFrame, airQualityStationDataFrame, how = "left", on = "location")

print(targetDataFrame)

"""

date.utc location parameter value coordinates.latitude coordinates.longitude

0 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 51.49467 -0.13193

1 2019-05-07 01:00:00+00:00 FR04014 no2 25.0 48.83724 2.39390

2 2019-05-07 01:00:00+00:00 FR04014 no2 25.0 48.83722 2.39390

3 2019-05-07 01:00:00+00:00 BETR801 pm25 12.5 51.20966 4.43182

4 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 51.20966 4.43182

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

4177 2019-06-20 23:00:00+00:00 FR04014 no2 21.8 48.83724 2.39390

4178 2019-06-20 23:00:00+00:00 FR04014 no2 21.8 48.83722 2.39390

4179 2019-06-21 00:00:00+00:00 London Westminster pm25 7.0 51.49467 -0.13193

4180 2019-06-21 00:00:00+00:00 FR04014 no2 20.0 48.83724 2.39390

4181 2019-06-21 00:00:00+00:00 FR04014 no2 20.0 48.83722 2.39390

[4182 rows x 6 columns]

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas 명령을 실행했다.

[PYTHON/PANDAS] DataFrame 클래스 : reset_index 메소드에서 level 인자를 사용해 모든 수준의 인덱스를 열로 변환하기

■ DataFrame 클래스의 reset_index 메소드에서 level 인자를 사용해 모든 수준의 인덱스를 열로 변환하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.read_csv("air_quality_no2_long.csv", parse_dates = True)

dataFrame2 = dataFrame1[["date.utc", "location","parameter", "value"]]

dataFrame3 = pd.read_csv("air_quality_pm25_long.csv",parse_dates = True)

dataFrame4 = dataFrame3[["date.utc", "location","parameter", "value"]]

dataFrame5 = pd.concat([dataFrame2, dataFrame4], keys = ["NO2", "PM25"])

dataFrame6 = dataFrame5.reset_index(level = 0)

print(dataFrame6.head())

"""
  level_0                   date.utc location parameter  value
0     NO2  2019-06-21 00:00:00+00:00  FR04014       no2   20.0
1     NO2  2019-06-20 23:00:00+00:00  FR04014       no2   21.8
2     NO2  2019-06-20 22:00:00+00:00  FR04014       no2   26.5
3     NO2  2019-06-20 21:00:00+00:00  FR04014       no2   24.9
4     NO2  2019-06-20 20:00:00+00:00  FR04014       no2   21.4
"""

import pandas as pd

dataFrame1 = pd.read_csv("air_quality_no2_long.csv", parse_dates = True)

dataFrame2 = dataFrame1[["date.utc", "location","parameter", "value"]]

dataFrame3 = pd.read_csv("air_quality_pm25_long.csv",parse_dates = True)

dataFrame4 = dataFrame3[["date.utc", "location","parameter", "value"]]

dataFrame5 = pd.concat([dataFrame2, dataFrame4], keys = ["NO2", "PM25"])

dataFrame6 = dataFrame5.reset_index(level = 0)

print(dataFrame6.head())

"""

level_0 date.utc location parameter value

0 NO2 2019-06-21 00:00:00+00:00 FR04014 no2 20.0

1 NO2 2019-06-20 23:00:00+00:00 FR04014 no2 21.8

2 NO2 2019-06-20 22:00:00+00:00 FR04014 no2 26.5

3 NO2 2019-06-20 21:00:00+00:00 FR04014 no2 24.9

4 NO2 2019-06-20 20:00:00+00:00 FR04014 no2 21.4

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※

[PYTHON/PANDAS] concat 함수 : keys 인자를 사용해 N개의 DataFrame 객체를 순서대로 결합하기

■ concat 함수의 keys 인자를 사용해 N개의 DataFrame 객체를 순서대로 결합하는 방법을 보여준다. ▶ main.py


import pandas as pd

no2DataFrame1 = pd.read_csv("air_quality_no2_long.csv", parse_dates = True)

no2DataFrame2 = no2DataFrame1[["date.utc", "location","parameter", "value"]]

pm25DataFrame1 = pd.read_csv("air_quality_pm25_long.csv",parse_dates = True)

pm25DataFrame2 = pm25DataFrame1[["date.utc", "location","parameter", "value"]]

concatDataFrame = pd.concat([no2DataFrame2, pm25DataFrame2], keys = ["NO2", "PM25"])

print(concatDataFrame.head())

"""
                        date.utc location parameter  value
NO2 0  2019-06-21 00:00:00+00:00  FR04014       no2   20.0
    1  2019-06-20 23:00:00+00:00  FR04014       no2   21.8
    2  2019-06-20 22:00:00+00:00  FR04014       no2   26.5
    3  2019-06-20 21:00:00+00:00  FR04014       no2   24.9
    4  2019-06-20 20:00:00+00:00  FR04014       no2   21.4
"""

import pandas as pd

no2DataFrame1 = pd.read_csv("air_quality_no2_long.csv", parse_dates = True)

no2DataFrame2 = no2DataFrame1[["date.utc", "location","parameter", "value"]]

pm25DataFrame1 = pd.read_csv("air_quality_pm25_long.csv",parse_dates = True)

pm25DataFrame2 = pm25DataFrame1[["date.utc", "location","parameter", "value"]]

concatDataFrame = pd.concat([no2DataFrame2, pm25DataFrame2], keys = ["NO2", "PM25"])

print(concatDataFrame.head())

"""

date.utc location parameter value

NO2 0 2019-06-21 00:00:00+00:00 FR04014 no2 20.0

1 2019-06-20 23:00:00+00:00 FR04014 no2 21.8

2 2019-06-20 22:00:00+00:00 FR04014 no2 26.5

3 2019-06-20 21:00:00+00:00 FR04014 no2 24.9

4 2019-06-20 20:00:00+00:00 FR04014 no2 21.4

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install

[PYTHON/PANDAS] concat 함수 : N개의 DataFrame 객체를 순서대로 결합하기 1

■ concat 함수를 사용해 N개의 DataFrame 객체를 순서대로 결합하는 방법을 보여준다. ▶ main.py


import pandas as pd

no2DataFrame1 = pd.read_csv("air_quality_no2_long.csv", parse_dates = True)

no2DataFrame2 = no2DataFrame1[["date.utc", "location","parameter", "value"]]

pm25DataFrame1 = pd.read_csv("air_quality_pm25_long.csv",parse_dates = True)

pm25DataFrame2 = pm25DataFrame1[["date.utc", "location","parameter", "value"]]

concatDataFrame = pd.concat([no2DataFrame2, pm25DataFrame2], axis = 0) # axis : 0(행), 1(컬럼)

print(concatDataFrame.head())

"""
                    date.utc location parameter  value
0  2019-06-21 00:00:00+00:00  FR04014       no2   20.0
1  2019-06-20 23:00:00+00:00  FR04014       no2   21.8
2  2019-06-20 22:00:00+00:00  FR04014       no2   26.5
3  2019-06-20 21:00:00+00:00  FR04014       no2   24.9
4  2019-06-20 20:00:00+00:00  FR04014       no2   21.4
"""

import pandas as pd

no2DataFrame1 = pd.read_csv("air_quality_no2_long.csv", parse_dates = True)

no2DataFrame2 = no2DataFrame1[["date.utc", "location","parameter", "value"]]

pm25DataFrame1 = pd.read_csv("air_quality_pm25_long.csv",parse_dates = True)

pm25DataFrame2 = pm25DataFrame1[["date.utc", "location","parameter", "value"]]

concatDataFrame = pd.concat([no2DataFrame2, pm25DataFrame2], axis = 0) # axis : 0(행), 1(컬럼)

print(concatDataFrame.head())

"""

date.utc location parameter value

0 2019-06-21 00:00:00+00:00 FR04014 no2 20.0

1 2019-06-20 23:00:00+00:00 FR04014 no2 21.8

2 2019-06-20 22:00:00+00:00 FR04014 no2 26.5

3 2019-06-20 21:00:00+00:00 FR04014 no2 24.9

4 2019-06-20 20:00:00+00:00 FR04014 no2 21.4

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas 명령을

[PYTHON/PANDAS] DataFrame 클래스 : melt 메소드에서 id_vars/value_vars/value_name/var_name 인자 사용하기

■ DataFrame 클래스의 melt 메소드에서 id_vars/value_vars/value_name/var_name 인자를 사용하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1[dataFrame1["parameter"] == "no2"] # 3447건

dataFrame3 = dataFrame2.pivot(columns = "location", values = "value")

dataFrame4 = dataFrame3.reset_index()

print(dataFrame4)

"""
location                  date.utc  BETR801  FR04014  London Westminster
0        2019-04-09 01:00:00+00:00     22.5     24.4                 NaN
1        2019-04-09 02:00:00+00:00     53.5     27.4                67.0
2        2019-04-09 03:00:00+00:00     54.5     34.2                67.0
3        2019-04-09 04:00:00+00:00     34.5     48.5                41.0
4        2019-04-09 05:00:00+00:00     46.5     59.5                41.0
...                            ...      ...      ...                 ...
1700     2019-06-20 20:00:00+00:00      NaN     21.4                 NaN
1701     2019-06-20 21:00:00+00:00      NaN     24.9                 NaN
1702     2019-06-20 22:00:00+00:00      NaN     26.5                 NaN
1703     2019-06-20 23:00:00+00:00      NaN     21.8                 NaN
1704     2019-06-21 00:00:00+00:00      NaN     20.0                 NaN

[1705 rows x 4 columns]
"""

print()

dataFrame5 = dataFrame4.melt(
    id_vars    = "date.utc",
    value_vars = ["BETR801", "FR04014", "London Westminster"],
    value_name = "NO_2",
    var_name   = "id_location"
)

print(dataFrame5)

"""
                      date.utc         id_location  NO_2
0    2019-04-09 01:00:00+00:00             BETR801  22.5
1    2019-04-09 02:00:00+00:00             BETR801  53.5
2    2019-04-09 03:00:00+00:00             BETR801  54.5
3    2019-04-09 04:00:00+00:00             BETR801  34.5
4    2019-04-09 05:00:00+00:00             BETR801  46.5
...                        ...                 ...   ...
5110 2019-06-20 20:00:00+00:00  London Westminster   NaN
5111 2019-06-20 21:00:00+00:00  London Westminster   NaN
5112 2019-06-20 22:00:00+00:00  London Westminster   NaN
5113 2019-06-20 23:00:00+00:00  London Westminster   NaN
5114 2019-06-21 00:00:00+00:00  London Westminster   NaN

[5115 rows x 3 columns]
"""

import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1[dataFrame1["parameter"] == "no2"] # 3447건

dataFrame3 = dataFrame2.pivot(columns = "location", values = "value")

dataFrame4 = dataFrame3.reset_index()

print(dataFrame4)

"""

location date.utc BETR801 FR04014 London Westminster

0 2019-04-09 01:00:00+00:00 22.5 24.4 NaN

1 2019-04-09 02:00:00+00:00 53.5 27.4 67.0

2 2019-04-09 03:00:00+00:00 54.5 34.2 67.0

3 2019-04-09 04:00:00+00:00 34.5 48.5 41.0

4 2019-04-09 05:00:00+00:00 46.5 59.5 41.0

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

1700 2019-06-20 20:00:00+00:00 NaN 21.4 NaN

1701 2019-06-20 21:00:00+00:00 NaN 24.9 NaN

1702 2019-06-20 22:00:00+00:00 NaN 26.5 NaN

1703 2019-06-20 23:00:00+00:00 NaN 21.8 NaN

1704 2019-06-21 00:00:00+00:00 NaN 20.0 NaN

[1705 rows x 4 columns]

"""

print()

dataFrame5 = dataFrame4.melt(

id_vars = "date.utc",

value_vars = ["BETR801", "FR04014", "London Westminster"],

value_name = "NO_2",

var_name = "id_location"

)

print(dataFrame5)

"""

date.utc id_location NO_2

0 2019-04-09 01:00:00+00:00 BETR801 22.5

1 2019-04-09 02:00:00+00:00 BETR801 53.5

2 2019-04-09 03:00:00+00:00 BETR801 54.5

3 2019-04-09 04:00:00+00:00 BETR801 34.5

4 2019-04-09 05:00:00+00:00 BETR801 46.5

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

5110 2019-06-20 20:00:00+00:00 London Westminster NaN

5111 2019-06-20 21:00:00+00:00 London Westminster NaN

5112 2019-06-20 22:00:00+00:00 London Westminster NaN

5113 2019-06-20 23:00:00+00:00 London Westminster NaN

5114 2019-06-21 00:00:00+00:00 London Westminster NaN

[5115 rows x 3 columns]

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas 명령을 실행했다.

[PYTHON/PANDAS] DataFrame 클래스 : melt 메소드에서 id_vars 인자를 사용해 WIDE 포맷 데이터에서 LONG 포맷 데이터 구하기

■ DataFrame 클래스의 melt 메소드에서 id_vars 인자를 사용해 WIDE 포맷 데이터에서 LONG 포맷 데이터를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1[dataFrame1["parameter"] == "no2"] # 3447건

dataFrame3 = dataFrame2.pivot(columns = "location", values = "value")

dataFrame4 = dataFrame3.reset_index()

print(dataFrame4)

"""
location                  date.utc  BETR801  FR04014  London Westminster
0        2019-04-09 01:00:00+00:00     22.5     24.4                 NaN
1        2019-04-09 02:00:00+00:00     53.5     27.4                67.0
2        2019-04-09 03:00:00+00:00     54.5     34.2                67.0
3        2019-04-09 04:00:00+00:00     34.5     48.5                41.0
4        2019-04-09 05:00:00+00:00     46.5     59.5                41.0
...                            ...      ...      ...                 ...
1700     2019-06-20 20:00:00+00:00      NaN     21.4                 NaN
1701     2019-06-20 21:00:00+00:00      NaN     24.9                 NaN
1702     2019-06-20 22:00:00+00:00      NaN     26.5                 NaN
1703     2019-06-20 23:00:00+00:00      NaN     21.8                 NaN
1704     2019-06-21 00:00:00+00:00      NaN     20.0                 NaN

[1705 rows x 4 columns]
"""

print()

dataFrame5 = dataFrame4.melt(id_vars = "date.utc")

print(dataFrame5)

"""
                      date.utc            location  value
0    2019-04-09 01:00:00+00:00             BETR801   22.5
1    2019-04-09 02:00:00+00:00             BETR801   53.5
2    2019-04-09 03:00:00+00:00             BETR801   54.5
3    2019-04-09 04:00:00+00:00             BETR801   34.5
4    2019-04-09 05:00:00+00:00             BETR801   46.5
...                        ...                 ...    ...
5110 2019-06-20 20:00:00+00:00  London Westminster    NaN
5111 2019-06-20 21:00:00+00:00  London Westminster    NaN
5112 2019-06-20 22:00:00+00:00  London Westminster    NaN
5113 2019-06-20 23:00:00+00:00  London Westminster    NaN
5114 2019-06-21 00:00:00+00:00  London Westminster    NaN

[5115 rows x 3 columns]
"""

import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1[dataFrame1["parameter"] == "no2"] # 3447건

dataFrame3 = dataFrame2.pivot(columns = "location", values = "value")

dataFrame4 = dataFrame3.reset_index()

print(dataFrame4)

"""

location date.utc BETR801 FR04014 London Westminster

0 2019-04-09 01:00:00+00:00 22.5 24.4 NaN

1 2019-04-09 02:00:00+00:00 53.5 27.4 67.0

2 2019-04-09 03:00:00+00:00 54.5 34.2 67.0

3 2019-04-09 04:00:00+00:00 34.5 48.5 41.0

4 2019-04-09 05:00:00+00:00 46.5 59.5 41.0

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

1700 2019-06-20 20:00:00+00:00 NaN 21.4 NaN

1701 2019-06-20 21:00:00+00:00 NaN 24.9 NaN

1702 2019-06-20 22:00:00+00:00 NaN 26.5 NaN

1703 2019-06-20 23:00:00+00:00 NaN 21.8 NaN

1704 2019-06-21 00:00:00+00:00 NaN 20.0 NaN

[1705 rows x 4 columns]

"""

print()

dataFrame5 = dataFrame4.melt(id_vars = "date.utc")

print(dataFrame5)

"""

date.utc location value

0 2019-04-09 01:00:00+00:00 BETR801 22.5

1 2019-04-09 02:00:00+00:00 BETR801 53.5

2 2019-04-09 03:00:00+00:00 BETR801 54.5

3 2019-04-09 04:00:00+00:00 BETR801 34.5

4 2019-04-09 05:00:00+00:00 BETR801 46.5

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

5110 2019-06-20 20:00:00+00:00 London Westminster NaN

5111 2019-06-20 21:00:00+00:00 London Westminster NaN

5112 2019-06-20 22:00:00+00:00 London Westminster NaN

5113 2019-06-20 23:00:00+00:00 London Westminster NaN

5114 2019-06-21 00:00:00+00:00 London Westminster NaN

[5115 rows x 3 columns]

"""

▶ requirements.txt

[PYTHON/PANDAS] DataFrame 클래스 : pivot 메소드에서 columns/values 인자를 사용해 LONG 포맷 데이터에서 WIDE 포맷 데이터 구하기 2

■ DataFrame 클래스의 pivot 메소드에서 columns/values 인자를 사용해 LONG 포맷 데이터에서 WIDE 포맷 데이터를 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1[dataFrame1["parameter"] == "no2"] # 3447건

dataFrame3 = dataFrame2.pivot(columns = "location", values = "value")

print(dataFrame3)

"""
location                   BETR801  FR04014  London Westminster
date.utc
2019-04-09 01:00:00+00:00     22.5     24.4                 NaN
2019-04-09 02:00:00+00:00     53.5     27.4                67.0
2019-04-09 03:00:00+00:00     54.5     34.2                67.0
2019-04-09 04:00:00+00:00     34.5     48.5                41.0
2019-04-09 05:00:00+00:00     46.5     59.5                41.0
...                            ...      ...                 ...
2019-06-20 20:00:00+00:00      NaN     21.4                 NaN
2019-06-20 21:00:00+00:00      NaN     24.9                 NaN
2019-06-20 22:00:00+00:00      NaN     26.5                 NaN
2019-06-20 23:00:00+00:00      NaN     21.8                 NaN
2019-06-21 00:00:00+00:00      NaN     20.0                 NaN

[1705 rows x 3 columns]
"""

import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1[dataFrame1["parameter"] == "no2"] # 3447건

dataFrame3 = dataFrame2.pivot(columns = "location", values = "value")

print(dataFrame3)

"""

location BETR801 FR04014 London Westminster

date.utc

2019-04-09 01:00:00+00:00 22.5 24.4 NaN

2019-04-09 02:00:00+00:00 53.5 27.4 67.0

2019-04-09 03:00:00+00:00 54.5 34.2 67.0

2019-04-09 04:00:00+00:00 34.5 48.5 41.0

2019-04-09 05:00:00+00:00 46.5 59.5 41.0

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

2019-06-20 20:00:00+00:00 NaN 21.4 NaN

2019-06-20 21:00:00+00:00 NaN 24.9 NaN

2019-06-20 22:00:00+00:00 NaN 26.5 NaN

2019-06-20 23:00:00+00:00 NaN 21.8 NaN

2019-06-21 00:00:00+00:00 NaN 20.0 NaN

[1705 rows x 3 columns]

"""

▶ requirements.txt

[PYTHON/PANDAS] DataFrame 클래스 : reset_index 메소드를 사용해 신규 인덱스 추가하기

■ DataFrame 클래스의 reset_index 메소드를 사용해 신규 인덱스를 추가하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1.pivot_table(values = "value", index = "location", columns = "parameter", aggfunc = "mean", margins = True)

dataFrame3 = dataFrame2.reset_index()

print(dataFrame3)

"""
parameter            location        no2       pm25        All
0                     BETR801  26.950920  23.169492  24.982353
1                     FR04014  29.374284        NaN  29.374284
2          London Westminster  29.740050  13.443568  21.491708
3                         All  29.430316  14.386849  24.222743
"""

import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1.pivot_table(values = "value", index = "location", columns = "parameter", aggfunc = "mean", margins = True)

dataFrame3 = dataFrame2.reset_index()

print(dataFrame3)

"""

parameter location no2 pm25 All

0 BETR801 26.950920 23.169492 24.982353

1 FR04014 29.374284 NaN 29.374284

2 London Westminster 29.740050 13.443568 21.491708

3 All 29.430316 14.386849 24.222743

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas 명령을

[PYTHON/PANDAS] DataFrame 클래스 : groupby 메소드를 사용해 피벗 데이터 만들기

■ DataFrame 클래스의 groupby 메소드를 사용해 피벗 데이터를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1.pivot_table(values = "value", index = "location", columns = "parameter", aggfunc = "mean", margins = True)

print(dataFrame2)

"""
parameter                 no2       pm25        All
location
BETR801             26.950920  23.169492  24.982353
FR04014             29.374284        NaN  29.374284
London Westminster  29.740050  13.443568  21.491708
All                 29.430316  14.386849  24.222743
"""

print()

dataFrame3 = dataFrame1.groupby(["parameter", "location"])[["value"]].mean()

print(dataFrame3)

"""
                                  value
parameter location
no2       BETR801             26.950920
          FR04014             29.374284
          London Westminster  29.740050
pm25      BETR801             23.169492
          London Westminster  13.443568
"""

import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1.pivot_table(values = "value", index = "location", columns = "parameter", aggfunc = "mean", margins = True)

print(dataFrame2)

"""

parameter no2 pm25 All

location

BETR801 26.950920 23.169492 24.982353

FR04014 29.374284 NaN 29.374284

London Westminster 29.740050 13.443568 21.491708

All 29.430316 14.386849 24.222743

"""

print()

dataFrame3 = dataFrame1.groupby(["parameter", "location"])[["value"]].mean()

print(dataFrame3)

"""

value

parameter location

no2 BETR801 26.950920

FR04014 29.374284

London Westminster 29.740050

pm25 BETR801 23.169492

London Westminster 13.443568

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas 명령을

[PYTHON/PANDAS] DataFrame 클래스 : pivot_table 메소드에서 margins 인자를 사용해 소계/총계 표시하기

■ DataFrame 클래스의 pivot_table 메소드에서 margins 인자를 사용해 소계/총계를 표시하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1.pivot_table(values = "value", index = "location", columns = "parameter", aggfunc = "mean", margins = True)

print(dataFrame2)

"""
parameter                 no2       pm25        All
location
BETR801             26.950920  23.169492  24.982353
FR04014             29.374284        NaN  29.374284
London Westminster  29.740050  13.443568  21.491708
All                 29.430316  14.386849  24.222743
"""

import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1.pivot_table(values = "value", index = "location", columns = "parameter", aggfunc = "mean", margins = True)

print(dataFrame2)

"""

parameter no2 pm25 All

location

BETR801 26.950920 23.169492 24.982353

FR04014 29.374284 NaN 29.374284

London Westminster 29.740050 13.443568 21.491708

All 29.430316 14.386849 24.222743

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install pandas

[PYTHON/PANDAS] DataFrame 클래스 : pivot_table 메소드에서 values/index/columns/aggfunc 인자를 사용해 피벗 데이터 만들기 1

■ DataFrame 클래스의 pivot_table 메소드에서 values/index/columns/aggfunc 인자를 사용해 피벗 데이터를 만드는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1.pivot_table(values = "value", index = "location", columns = "parameter", aggfunc = "mean")

print(dataFrame2)

"""
parameter                 no2       pm25
location
BETR801             26.950920  23.169492
FR04014             29.374284        NaN
London Westminster  29.740050  13.443568
"""

import pandas as pd

dataFrame1 = pd.read_csv("air_quality_long.csv", index_col = "date.utc", parse_dates = True) # 5272건

dataFrame2 = dataFrame1.pivot_table(values = "value", index = "location", columns = "parameter", aggfunc = "mean")

print(dataFrame2)

"""

parameter no2 pm25

location

BETR801 26.950920 23.169492

FR04014 29.374284 NaN

London Westminster 29.740050 13.443568

"""

▶ requirements.txt


numpy==2.1.2
pandas==2.2.3
python-dateutil==2.9.0.post0
pytz==2024.2
six==1.16.0
tzdata==2024.2

numpy==2.1.2

pandas==2.2.3

python-dateutil==2.9.0.post0

pytz==2024.2

six==1.16.0

tzdata==2024.2

※ pip install