■ dir 함수를 사용해 PlotAccessor 클래스의 메소드를 출력하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
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 |
※ pip install pandas matplotlib 명령을
더 읽기
■ 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() |
▶ 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 |
※ pip install pandas matplotlib
더 읽기
■ 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() |
▶ 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 |
※ pip install pandas matplotlib
더 읽기
■ 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() |
▶ 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 |
※ pip install pandas matplotlib
더 읽기
■ read_csv 함수에서 index_col/parse_dates 인자를 사용해 CSV 파일 데이터를 로드하는 방법을 보여준다. ▶ main.py
|
|
import pandas as pd dataFrame = pd.read_csv("air_quality_no2.csv", index_col = 0, parse_dates = True) |
▶ 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 |
※ pip install pandans
더 읽기
■ DataFrame 클래스의 iloc 속성을 사용해 값을 설정하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
import pandas as pd dataFrame = pd.read_excel("titanic.xlsx", sheet_name = "passengers") dataFrame.iloc[0:3, 3] = "anonymous" print(dataFrame) """ PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 anonymous male 22.0 1 0 A/5 21171 7.2500 NaN S 1 2 1 1 anonymous female 38.0 1 0 PC 17599 71.2833 C85 C 2 3 1 3 anonymous female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S .. ... ... ... ... ... ... ... ... ... ... ... ... 886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S 887 888 1 1 Graham, Miss Margaret Edith female 19.0 0 0 112053 30.0000 B42 S 888 889 0 3 Johnston, Miss Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S 889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C 890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q [891 rows x 12 columns] """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install "pandas[excel]" 명령을 실행했다.
더 읽기
■ DataFrame 클래스의 iloc 속성을 사용해 부분 데이터 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 |
import pandas as pd dataFrame1 = pd.read_excel("titanic.xlsx", sheet_name = "passengers") dataFrame2 = dataFrame1.iloc[9:25, 2:5] print(dataFrame2) """ Pclass Name Sex 9 2 Nasser, Mrs. Nicholas (Adele Achem) female 10 3 Sandstrom, Miss Marguerite Rut female 11 1 Bonnell, Miss Elizabeth female 12 3 Saundercock, Mr. William Henry male 13 3 Andersson, Mr. Anders Johan male 14 3 Vestrom, Miss Hulda Amanda Adolfina female 15 2 Hewlett, Mrs. (Mary D Kingcome) female 16 3 Rice, Master Eugene male 17 2 Williams, Mr. Charles Eugene male 18 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 19 3 Masselmani, Mrs. Fatima female 20 2 Fynney, Mr. Joseph J male 21 2 Beesley, Mr. Lawrence male 22 3 McGowan, Miss Anna "Annie" female 23 1 Sloper, Mr. William Thompson male 24 3 Palsson, Miss Torborg Danira female """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install
더 읽기
■ DataFrame 클래스의 loc 속성을 사용해 특정 조건을 만족하는 특정 컬럼 Series 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 |
import pandas as pd dataFrame = pd.read_excel("titanic.xlsx", sheet_name = "passengers") series = dataFrame.loc[dataFrame["Age"] > 35, "Name"] print(series) """ 1 Cumings, Mrs. John Bradley (Florence Briggs Th... 6 McCarthy, Mr. Timothy J 11 Bonnell, Miss Elizabeth 13 Andersson, Mr. Anders Johan 15 Hewlett, Mrs. (Mary D Kingcome) ... 865 Bystrom, Mrs. (Karolina) 871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 873 Vander Cruyssen, Mr. Victor 879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) 885 Rice, Mrs. William (Margaret Norton) Name: Name, Length: 217, dtype: object """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
더 읽기
■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼에서 값을 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
import pandas as pd dataFrame1 = pd.read_excel("titanic.xlsx", sheet_name = "passengers") dataFrame2 = dataFrame[dataFrame1["Age"].notna()] print(dataFrame2) """ PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S .. ... ... ... ... ... ... ... ... ... ... ... ... 885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q 886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S 887 888 1 1 Graham, Miss Margaret Edith female 19.0 0 0 112053 30.0000 B42 S 889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C 890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q [714 rows x 12 columns] """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※
더 읽기
■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼에서 값을 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
import pandas as pd dataFrame1 = pd.read_excel("titanic.xlsx", sheet_name = "passengers") dataFrame2 = dataFrame[dataFrame1["Age"].notna()] print(dataFrame2) """ PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S .. ... ... ... ... ... ... ... ... ... ... ... ... 885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q 886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S 887 888 1 1 Graham, Miss Margaret Edith female 19.0 0 0 112053 30.0000 B42 S 889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C 890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q [714 rows x 12 columns] """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※
더 읽기
■ Series 클래스의 notna 메소드를 사용해 값 여부 Series 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
import pandas as pd dataFrame = pd.read_excel("titanic.xlsx", sheet_name = "passengers") series1 = dataFrame["Age"] series2 = series1.notna() print(series2) """ 0 True 1 True 2 True 3 True 4 True ... 886 True 887 True 888 False 889 True 890 True Name: Age, Length: 891, dtype: bool """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install
더 읽기
■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼에서 특정 값들을 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 |
import pandas as pd dataFrame1 = pd.read_excel("titanic.xlsx", sheet_name = "passengers") series = dataFrame1["Pclass"] dataFrame2 = dataFrame1[(dataFrame1["Pclass"] == 2) | (dataFrame1["Pclass"] == 3)] print(dataFrame2) """ PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 2 3 1 3 Heikkinen, Miss Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S .. ... ... ... ... ... ... ... ... ... ... ... ... 884 885 0 3 Sutehall, Mr. Henry Jr male 25.0 0 0 SOTON/OQ 392076 7.0500 NaN S 885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q 886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S 888 889 0 3 Johnston, Miss Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S 890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q [675 rows x 12 columns] """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
더 읽기
■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼에서 특정 값들을 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 |
import pandas as pd dataFrame1 = pd.read_excel("titanic.xlsx", sheet_name = "passengers") series = dataFrame1["Pclass"] dataFrame2 = dataFrame1[series.isin([2, 3])] print(dataFrame2) """ PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 2 3 1 3 Heikkinen, Miss Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S .. ... ... ... ... ... ... ... ... ... ... ... ... 884 885 0 3 Sutehall, Mr. Henry Jr male 25.0 0 0 SOTON/OQ 392076 7.0500 NaN S 885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q 886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S 888 889 0 3 Johnston, Miss Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S 890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q [675 rows x 12 columns] """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
더 읽기
■ Series 클래스의 isin 메소드를 사용해 지정 값 리스트에 포함 여부를 갖는 Series 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
import pandas as pd dataFrame = pd.read_excel("titanic.xlsx", sheet_name = "passengers") series1 = dataFrame["Pclass"] series2 = series1.isin([2, 3]) print(series2) """ 0 True 1 False 2 True 3 False 4 True ... 886 True 887 False 888 True 889 False 890 True Name: Pclass, Length: 891, dtype: bool """ |
▶ requirements.txt
더 읽기
■ DataFrame 클래스에서 특정 조건에 해당하는 데이터를 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
import pandas as pd dataFrame1 = pd.read_excel("titanic.xlsx", sheet_name = "passengers") dataFrame2 = dataFrame1[dataFrame1["Age"] > 35] print(dataFrame2) """ PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S 11 12 1 1 Bonnell, Miss Elizabeth female 58.0 0 0 113783 26.5500 C103 S 13 14 0 3 Andersson, Mr. Anders Johan male 39.0 1 5 347082 31.2750 NaN S 15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 0 248706 16.0000 NaN S .. ... ... ... ... ... ... ... ... ... ... ... ... 865 866 1 2 Bystrom, Mrs. (Karolina) female 42.0 0 0 236852 13.0000 NaN S 871 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 1 11751 52.5542 D35 S 873 874 0 3 Vander Cruyssen, Mr. Victor male 47.0 0 0 345765 9.0000 NaN S 879 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 1 11767 83.1583 C50 C 885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q [217 rows x 12 columns] """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install
더 읽기
■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼들의 데이터를 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
import pandas as pd dataFrame1 = pd.read_excel("titanic.xlsx", sheet_name = "passengers") dataFrame2 = dataFrame1[["Age", "Sex"]] print(dataFrame2) """ Age Sex 0 22.0 male 1 38.0 female 2 26.0 female 3 35.0 female 4 35.0 male .. ... ... 886 27.0 male 887 19.0 female 888 NaN female 889 26.0 male 890 32.0 male [891 rows x 2 columns] """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※
더 읽기
■ Series 클래스의 shape 속성을 사용해 데이터 차원을 구하는 방법을 보여준다. ▶ main.py
|
|
import pandas as pd dataFrame = pd.read_excel("titanic.xlsx", sheet_name = "passengers") series = dataFrame["Age"] shapeTuple = series.shape print(shapeTuple) """ (891,) """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install "pandas[excel]" 명령을
더 읽기
■ DataFrame 클래스의 shape 속성을 사용해 데이터 차원을 구하는 방법을 보여준다. ▶ main.py
|
|
import pandas as pd dataFrame = pd.read_excel("titanic.xlsx", sheet_name = "passengers") shapeTuple = dataFrame.shape print(shapeTuple) """ (891, 12) """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install "pandas[excel]" 명령을
더 읽기
■ Series 클래스의 head 메소드를 사용해 선행 데이터를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 |
import pandas as pd dataFrame = pd.read_excel("titanic.xlsx", sheet_name = "passengers") series1 = dataFrame["Age"] series2 = series1.head() print(series2) """ 0 22.0 1 38.0 2 26.0 3 35.0 4 35.0 Name: Age, dtype: float64 """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install "pandas[excel]" 명령을
더 읽기
■ DataFrame 클래스의 info 메소드를 사용해 데이터 정보를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 |
import pandas as pd dataFrame1 = pd.read_excel("titanic.xlsx", sheet_name = "passengers") dataFrame2 = dataFrame1.info() print(dataFrame2) """ <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 PassengerId 891 non-null int64 1 Survived 891 non-null int64 2 Pclass 891 non-null int64 3 Name 891 non-null object 4 Sex 891 non-null object 5 Age 714 non-null float64 6 SibSp 891 non-null int64 7 Parch 891 non-null int64 8 Ticket 891 non-null object 9 Fare 891 non-null float64 10 Cabin 204 non-null object 11 Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB None """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install "pandas[excel]" 명령을
더 읽기
■ read_excel 함수의 sheet_name 인자를 사용해 엑셀 파일 데이터를 로드하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 |
import pandas as pd dataFrame = pd.read_excel("titanic.xlsx", sheet_name = "passengers") print(dataFrame) """ PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S .. ... ... ... ... ... ... ... ... ... ... ... ... 886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S 887 888 1 1 Graham, Miss Margaret Edith female 19.0 0 0 112053 30.0000 B42 S 888 889 0 3 Johnston, Miss Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S 889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C 890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q [891 rows x 12 columns] """ |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install "pandas[excel]"
더 읽기
■ DataFrame 클래스의 to_excel 메소드에서 sheet_name/index 인자를 사용해 엑셀 파일을 만드는 방법을 보여준다. ▶ main.py
|
|
import pandas as pd dataFrame = pd.read_csv("titanic.csv") dataFrame.to_excel("titanic.xlsx", sheet_name = "passengers", index = False) |
▶ requirements.txt
|
|
defusedxml==0.7.1 et_xmlfile==2.0.0 numpy==2.1.2 odfpy==1.4.1 openpyxl==3.1.5 pandas==2.2.3 python-calamine==0.2.3 python-dateutil==2.9.0.post0 pytz==2024.2 pyxlsb==1.0.10 six==1.16.0 tzdata==2024.2 xlrd==2.0.1 XlsxWriter==3.2.0 |
※ pip install
더 읽기
■ DataFrame 클래스의 dtypes 속성을 사용해 컬럼 데이터 타입 Series 객체를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 |
import pandas as pd dataFrame = pd.read_csv("titanic.csv") series = dataFrame.dtypes print(series) """ PassengerId int64 Survived int64 Pclass int64 Name object Sex object Age float64 SibSp int64 Parch int64 Ticket object Fare float64 Cabin object Embarked object dtype: object """ |
▶ 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 |
※ pip
더 읽기
■ DataFrame 클래스의 tail 메소드를 사용해 후행 데이터를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
import pandas as pd dataFrame1 = pd.read_csv("titanic.csv") dataFrame2 = dataFrame1.tail(8) print(dataFrame2) """ PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 883 884 0 2 Banfield, Mr. Frederick James male 28.0 0 0 C.A./SOTON 34068 10.500 NaN S 884 885 0 3 Sutehall, Mr. Henry Jr male 25.0 0 0 SOTON/OQ 392076 7.050 NaN S 885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.125 NaN Q 886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.000 NaN S 887 888 1 1 Graham, Miss Margaret Edith female 19.0 0 0 112053 30.000 B42 S 888 889 0 3 Johnston, Miss Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.450 NaN S 889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.000 C148 C 890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.750 NaN Q """ |
▶ 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 |
※ pip install pandas 명령을
더 읽기
■ DataFrame 클래스의 head 메소드를 사용해 선행 데이터를 구하는 방법을 보여준다. ▶ main.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
import pandas as pd dataFrame1 = pd.read_csv("titanic.csv") dataFrame2 = dataFrame1.head(8) print(dataFrame2) """ PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q 6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S 7 8 0 3 Palsson, Master Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S """ |
▶ 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 |
※ pip install pandas 명령을
더 읽기
