EXCEL Archives - icodebroker

[C#/COMMON/.NET6] 엑셀에서 OLLAMA 질문 사용자 함수 추가하기

■ 엑셀에서 OLLAMA에게 질문을 하는 사용자 함수를 추가하는 방법을 보여준다. ▶ TestLibrary.csproj


<Project Sdk="Microsoft.NET.Sdk">
    <PropertyGroup>
        <TargetFramework>net6.0-windows</TargetFramework>
        <ImplicitUsings>disable</ImplicitUsings>
        <Nullable>disable</Nullable>
    </PropertyGroup>
    <ItemGroup>
        <PackageReference Include="ExcelDna.AddIn" Version="1.8.0" />
    </ItemGroup>
</Project>

<TargetFramework>net6.0-windows</TargetFramework>

<ImplicitUsings>disable</ImplicitUsings>

<Nullable>disable</Nullable>

</PropertyGroup>

</ItemGroup>

</Project>

▶ launchSettings.json


{
  "profiles": {
    "Excel": {
      "commandName": "Executable",
      "executablePath": "C:\\Program Files\\Microsoft Office\\root\\Office16\\EXCEL.EXE",
      "commandLineArgs": "/x \"TestLibrary-AddIn64.xll\""
    }
  }
}

{

"profiles": {

"Excel": {

"commandName": "Executable",

"executablePath": "C:\\Program Files\\Microsoft Office\\root\\Office16\\EXCEL.EXE",

"commandLineArgs": "/x \"TestLibrary-AddIn64.xll\""

}

▶ OllamaClient.cs


using System.Net.Http;
using System.Text;
using System.Text.Json;

namespace TestLibrary;

/// <summary>
/// OLLAMA 클라이언트
/// </summary>
public class OllamaClient
{
    //////////////////////////////////////////////////////////////////////////////////////////////////// Field
    ////////////////////////////////////////////////////////////////////////////////////////// Private

    #region Field

    /// <summary>
    /// 기본 URL
    /// </summary>
    private readonly string baseURL;

    /// <summary>
    /// 모델명
    /// </summary>
    private readonly string modelName;

    /// <summary>
    /// HTTP 클라이언트
    /// </summary>
    private readonly HttpClient client;

    #endregion

    //////////////////////////////////////////////////////////////////////////////////////////////////// Constructor
    ////////////////////////////////////////////////////////////////////////////////////////// Public

    #region 생성자 - OllamaClient(baseURL, modelName)

    /// <summary>
    /// 생성자
    /// </summary>
    /// <param name="baseURL">기본 URL</param>
    /// <param name="modelName">모델명</param>
    public OllamaClient(string baseURL = "http://localhost:11434", string modelName = "bnksys/eeve-yanolja-v1:latest")
    {
        this.baseURL = baseURL;
        this.modelName = modelName;

        this.client = new HttpClient();
    }

    #endregion

    //////////////////////////////////////////////////////////////////////////////////////////////////// Method
    ////////////////////////////////////////////////////////////////////////////////////////// Public

    #region 질문하기 - Ask(question)

    /// <summary>
    /// 질문하기
    /// </summary>
    /// <param name="question">질문</param>
    /// <returns>답변</returns>
    public string Ask(string question)
    {
        var requestContent = new
        {
            model  = this.modelName,
            prompt = question,
            stream = false,
            raw    = false
        };

        string json = JsonSerializer.Serialize(requestContent);

        StringContent stringContent = new StringContent(json, Encoding.UTF8, "application/json");

        using(HttpResponseMessage httpResponseMessage = client.PostAsync($"{baseURL}/api/generate", stringContent).Result)
        {
            httpResponseMessage.EnsureSuccessStatusCode();

            string respnseJSON = httpResponseMessage.Content.ReadAsStringAsync().Result;

            using(JsonDocument jsonElement = JsonDocument.Parse(respnseJSON))
            {
                JsonElement rootJSONElement = jsonElement.RootElement;

                if(rootJSONElement.TryGetProperty("response", out JsonElement responseElement))
                {
                    return responseElement.GetString() ?? string.Empty;
                }

                return string.Empty;
            }
        }
    }

    #endregion
}

100

101

using System.Net.Http;

using System.Text;

using System.Text.Json;

namespace TestLibrary;

/// <summary>

/// OLLAMA 클라이언트

/// </summary>

public class OllamaClient

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Field

////////////////////////////////////////////////////////////////////////////////////////// Private

#region Field

/// <summary>

/// 기본 URL

/// </summary>

private readonly string baseURL;

/// <summary>

/// 모델명

/// </summary>

private readonly string modelName;

/// <summary>

/// HTTP 클라이언트

/// </summary>

private readonly HttpClient client;

#endregion

//////////////////////////////////////////////////////////////////////////////////////////////////// Constructor

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 생성자 - OllamaClient(baseURL, modelName)

/// <summary>

/// 생성자

/// </summary>

/// <param name="baseURL">기본 URL</param>

/// <param name="modelName">모델명</param>

public OllamaClient(string baseURL = "http://localhost:11434", string modelName = "bnksys/eeve-yanolja-v1:latest")

{

this.baseURL = baseURL;

this.modelName = modelName;

this.client = new HttpClient();

}

#endregion

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Public

#region 질문하기 - Ask(question)

/// <summary>

/// 질문하기

/// </summary>

/// <param name="question">질문</param>

/// <returns>답변</returns>

public string Ask(string question)

{

var requestContent = new

{

model = this.modelName,

prompt = question,

stream = false,

raw = false

};

string json = JsonSerializer.Serialize(requestContent);

StringContent stringContent = new StringContent(json, Encoding.UTF8, "application/json");

using(HttpResponseMessage httpResponseMessage = client.PostAsync($"{baseURL}/api/generate", stringContent).Result)

{

httpResponseMessage.EnsureSuccessStatusCode();

string respnseJSON = httpResponseMessage.Content.ReadAsStringAsync().Result;

using(JsonDocument jsonElement = JsonDocument.Parse(respnseJSON))

{

JsonElement rootJSONElement = jsonElement.RootElement;

if(rootJSONElement.TryGetProperty("response", out JsonElement responseElement))

{

return responseElement.GetString() ?? string.Empty;

}

return string.Empty;

}

#endregion

}

▶ CustomFunction.cs


using ExcelDna.Integration;

namespace TestLibrary;

/// <summary>
/// 커스텀 함수
/// </summary>
public static class CustomFunction
{
    //////////////////////////////////////////////////////////////////////////////////////////////////// Method
    ////////////////////////////////////////////////////////////////////////////////////////// Public

    #region OLLAMA 질문하기 - AskOllama(question)

    /// <summary>
    /// OLLAMA 질문하기
    /// </summary>
    /// <param name="question">질문</param>
    /// <returns>답변</returns>
    [ExcelFunction(Description = "Ollama에게 질문을 합니다.")]
    public static string AskOllama(string question)
    {
        OllamaClient ollamaClient = new OllamaClient();

        string answer = ollamaClient.Ask(question);

        return answer;
    }

    #endregion
}

using ExcelDna.Integration;

namespace TestLibrary;

/// <summary>

/// 커스텀 함수

/// </summary>

public static class CustomFunction

{

//////////////////////////////////////////////////////////////////////////////////////////////////// Method

////////////////////////////////////////////////////////////////////////////////////////// Public

#region OLLAMA 질문하기 - AskOllama(question)

/// <summary>

/// OLLAMA 질문하기

/// </summary>

/// <param name="question">질문</param>

/// <returns>답변</returns>

[ExcelFunction(Description = "Ollama에게 질문을 합니다.")]

public static string AskOllama(string question)

{

OllamaClient ollamaClient = new OllamaClient();

string answer = ollamaClient.Ask(question);

return answer;

}

#endregion

}

[C#/COMMON/.NET6] ExcelDna.AddIn 누겟 설치하기

■ ExcelDna.AddIn 누겟을 설치하는 방법을 보여준다. 1. Visual Studio를 실행한다. 2. [도구] / [NuGet 패키지 관리자] / [패키지 관리자 콘솔] 메뉴를 실행한다.

[PYTHON/PANDAS] read_excel 함수 : index_col/na_values 인자를 사용해 EXCEL 파일 데이터 로드하기

■ read_excel 함수의 index_col/na_values 인자를 사용해 EXCEL 파일 데이터를 로드하는 방법을 보여준다. ▶ main.py


import pandas as pd
import numpy  as np

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

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

dataFrame1.to_excel("test.xlsx", sheet_name = "Sheet1")

print(dataFrame1)

"""
                   A         B         C         D
2000-01-01  0.053874  0.285479  2.200199 -0.497760
2000-01-02  0.382564  0.032303  2.176588  0.970032
2000-01-03 -0.960918  0.380824 -0.187437 -0.691698
2000-01-04 -0.854058 -1.280247 -1.469207 -1.037654
2000-01-05  0.141451 -0.025518  1.005225 -0.792409
...              ...       ...       ...       ...
2002-09-22 -0.827884  0.614906 -0.871884  1.219365
2002-09-23  0.083260  0.468676 -0.803836  0.798946
2002-09-24  0.908064 -0.526946  0.538067  0.067274
2002-09-25  1.715925 -1.253660 -0.327901 -2.050811
2002-09-26 -0.224802  0.335902 -1.158583 -0.413148

[1000 rows x 4 columns]
"""

print()

dataFrame2 = pd.read_excel("test.xlsx", "Sheet1", index_col = None, na_values = ["NA"])

print(dataFrame2)

"""
    Unnamed: 0         A         B         C         D
0   2000-01-01  0.053874  0.285479  2.200199 -0.497760
1   2000-01-02  0.382564  0.032303  2.176588  0.970032
2   2000-01-03 -0.960918  0.380824 -0.187437 -0.691698
3   2000-01-04 -0.854058 -1.280247 -1.469207 -1.037654
4   2000-01-05  0.141451 -0.025518  1.005225 -0.792409
..         ...       ...       ...       ...       ...
995 2002-09-22 -0.827884  0.614906 -0.871884  1.219365
996 2002-09-23  0.083260  0.468676 -0.803836  0.798946
997 2002-09-24  0.908064 -0.526946  0.538067  0.067274
998 2002-09-25  1.715925 -1.253660 -0.327901 -2.050811
999 2002-09-26 -0.224802  0.335902 -1.158583 -0.413148

[1000 rows x 5 columns]
"""

import pandas as pd

import numpy as np

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

dataFrame1 = pd.DataFrame(

np.random.randn(1000, 4),

index = datetimeIndex,

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

)

dataFrame1.to_excel("test.xlsx", sheet_name = "Sheet1")

print(dataFrame1)

"""

A B C D

2000-01-01 0.053874 0.285479 2.200199 -0.497760

2000-01-02 0.382564 0.032303 2.176588 0.970032

2000-01-03 -0.960918 0.380824 -0.187437 -0.691698

2000-01-04 -0.854058 -1.280247 -1.469207 -1.037654

2000-01-05 0.141451 -0.025518 1.005225 -0.792409

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

2002-09-22 -0.827884 0.614906 -0.871884 1.219365

2002-09-23 0.083260 0.468676 -0.803836 0.798946

2002-09-24 0.908064 -0.526946 0.538067 0.067274

2002-09-25 1.715925 -1.253660 -0.327901 -2.050811

2002-09-26 -0.224802 0.335902 -1.158583 -0.413148

[1000 rows x 4 columns]

"""

print()

dataFrame2 = pd.read_excel("test.xlsx", "Sheet1", index_col = None, na_values = ["NA"])

print(dataFrame2)

"""

Unnamed: 0 A B C D

0 2000-01-01 0.053874 0.285479 2.200199 -0.497760

1 2000-01-02 0.382564 0.032303 2.176588 0.970032

2 2000-01-03 -0.960918 0.380824 -0.187437 -0.691698

3 2000-01-04 -0.854058 -1.280247 -1.469207 -1.037654

4 2000-01-05 0.141451 -0.025518 1.005225 -0.792409

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

995 2002-09-22 -0.827884 0.614906 -0.871884 1.219365

996 2002-09-23 0.083260 0.468676 -0.803836 0.798946

997 2002-09-24 0.908064 -0.526946 0.538067 0.067274

998 2002-09-25 1.715925 -1.253660 -0.327901 -2.050811

999 2002-09-26 -0.224802 0.335902 -1.158583 -0.413148

[1000 rows x 5 columns]

"""

▶ requirements.txt


defusedxml==0.7.1
et_xmlfile==2.0.0
numpy==2.1.3
odfpy==1.4.1
openpyxl==3.1.5
packaging==24.2
pandas==2.2.3
python-calamine==0.3.1
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

defusedxml==0.7.1

et_xmlfile==2.0.0

numpy==2.1.3

odfpy==1.4.1

openpyxl==3.1.5

packaging==24.2

pandas==2.2.3

python-calamine==0.3.1

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]"

[PYTHON/PANDAS] StringMethods 클래스 : [] 연산자를 사용해 문자열 구하기

■ StringMethods 클래스에서 [] 연산자를 사용해 문자열을 구하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

stringMethods = dataFrame["sex"].str

series = stringMethods[0:1]

print(series)

"""
0      F
1      M
2      M
3      M
4      F
      ..
239    M
240    F
241    M
242    M
243    F
Name: sex, Length: 244, dtype: object
"""

import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

stringMethods = dataFrame["sex"].str

series = stringMethods[0:1]

print(series)

"""

0 F

1 M

2 M

3 M

4 F

239 M

240 F

241 M

242 M

243 F

Name: sex, Length: 244, dtype: object

"""

▶ requirements.txt


defusedxml==0.7.1
et_xmlfile==2.0.0
numpy==2.1.3
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

defusedxml==0.7.1

et_xmlfile==2.0.0

numpy==2.1.3

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.

[PYTHON/PANDAS] StringMethods 클래스 : find 메소드를 사용해 해당 문자열 찾기

■ StringMethods 클래스의 find 메소드를 사용해 해당 문자열을 찾는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

stringMethods = dataFrame["sex"].str

series = stringMethods.find("ale") # 없는 경우 -1을 반환한다.

print(series)

"""
0      3
1      1
2      1
3      1
4      3
      ..
239    1
240    3
241    1
242    1
243    3
Name: sex, Length: 244, dtype: int64
"""

import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

stringMethods = dataFrame["sex"].str

series = stringMethods.find("ale") # 없는 경우 -1을 반환한다.

print(series)

"""

0 3

1 1

2 1

3 1

4 3

239 1

240 3

241 1

242 1

243 3

Name: sex, Length: 244, dtype: int64

"""

▶ requirements.txt


defusedxml==0.7.1
et_xmlfile==2.0.0
numpy==2.1.3
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

defusedxml==0.7.1

et_xmlfile==2.0.0

numpy==2.1.3

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

[PYTHON/PANDAS] StringMethods 클래스 : rstrip 메소드를 사용해 문자열에서 후행 공백 제거하기

■ StringMethods 클래스의 rstrip 메소드를 사용해 문자열에서 후행 공백을 제거하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

stringMethods1 = dataFrame["time"].str

series = stringMethods1.rstrip()

stringMethods2 = series.str

dataFrame["time_length"] = stringMethods2.len()

print(dataFrame)

"""
     total_bill   tip     sex smoker   day    time  size  time_length
0         16.99  1.01  Female     No   Sun  Dinner     2            6
1         10.34  1.66    Male     No   Sun  Dinner     3            6
2         21.01  3.50    Male     No   Sun  Dinner     3            6
3         23.68  3.31    Male     No   Sun  Dinner     2            6
4         24.59  3.61  Female     No   Sun  Dinner     4            6
..          ...   ...     ...    ...   ...     ...   ...          ...
239       29.03  5.92    Male     No   Sat  Dinner     3            6
240       27.18  2.00  Female    Yes   Sat  Dinner     2            6
241       22.67  2.00    Male    Yes   Sat  Dinner     2            6
242       17.82  1.75    Male     No   Sat  Dinner     2            6
243       18.78  3.00  Female     No  Thur  Dinner     2            6

[244 rows x 8 columns]
"""

import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

stringMethods1 = dataFrame["time"].str

series = stringMethods1.rstrip()

stringMethods2 = series.str

dataFrame["time_length"] = stringMethods2.len()

print(dataFrame)

"""

total_bill tip sex smoker day time size time_length

0 16.99 1.01 Female No Sun Dinner 2 6

1 10.34 1.66 Male No Sun Dinner 3 6

2 21.01 3.50 Male No Sun Dinner 3 6

3 23.68 3.31 Male No Sun Dinner 2 6

4 24.59 3.61 Female No Sun Dinner 4 6

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

239 29.03 5.92 Male No Sat Dinner 3 6

240 27.18 2.00 Female Yes Sat Dinner 2 6

241 22.67 2.00 Male Yes Sat Dinner 2 6

242 17.82 1.75 Male No Sat Dinner 2 6

243 18.78 3.00 Female No Thur Dinner 2 6

[244 rows x 8 columns]

"""

▶ requirements.txt


defusedxml==0.7.1
et_xmlfile==2.0.0
numpy==2.1.3
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

defusedxml==0.7.1

et_xmlfile==2.0.0

numpy==2.1.3

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]"

[PYTHON/PANDAS] StringMethods 클래스 : len 메소드를 사용해 DataFrame 객체에 문자열 길이 신규 컬럼 추가하기

■ StringMethods 클래스의 len 메소드를 사용해 DataFrame 객체에 문자열 길이를 갖는 신규 컬럼을 추가하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

stringMethods = dataFrame["time"].str

dataFrame["time_length"] = stringMethods.len()

print(dataFrame)

"""
     total_bill   tip     sex smoker   day    time  size  time_length
0         16.99  1.01  Female     No   Sun  Dinner     2            6
1         10.34  1.66    Male     No   Sun  Dinner     3            6
2         21.01  3.50    Male     No   Sun  Dinner     3            6
3         23.68  3.31    Male     No   Sun  Dinner     2            6
4         24.59  3.61  Female     No   Sun  Dinner     4            6
..          ...   ...     ...    ...   ...     ...   ...          ...
239       29.03  5.92    Male     No   Sat  Dinner     3            6
240       27.18  2.00  Female    Yes   Sat  Dinner     2            6
241       22.67  2.00    Male    Yes   Sat  Dinner     2            6
242       17.82  1.75    Male     No   Sat  Dinner     2            6
243       18.78  3.00  Female     No  Thur  Dinner     2            6

[244 rows x 8 columns]
"""

import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

stringMethods = dataFrame["time"].str

dataFrame["time_length"] = stringMethods.len()

print(dataFrame)

"""

total_bill tip sex smoker day time size time_length

0 16.99 1.01 Female No Sun Dinner 2 6

1 10.34 1.66 Male No Sun Dinner 3 6

2 21.01 3.50 Male No Sun Dinner 3 6

3 23.68 3.31 Male No Sun Dinner 2 6

4 24.59 3.61 Female No Sun Dinner 4 6

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

239 29.03 5.92 Male No Sat Dinner 3 6

240 27.18 2.00 Female Yes Sat Dinner 2 6

241 22.67 2.00 Male Yes Sat Dinner 2 6

242 17.82 1.75 Male No Sat Dinner 2 6

243 18.78 3.00 Female No Thur Dinner 2 6

[244 rows x 8 columns]

"""

▶ requirements.txt


defusedxml==0.7.1
et_xmlfile==2.0.0
numpy==2.1.3
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

defusedxml==0.7.1

et_xmlfile==2.0.0

numpy==2.1.3

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

[PYTHON/PANDAS] DatetimeProperties 클래스 : to_period 메소드를 사용해 DataFrame 객체에서 두 날짜 컬럼 값 사이 월 수를 갖는 신규 컬럼 추가하기

■ DatetimeProperties 클래스의 to_period 메소드를 사용해 DataFrame 객체에서 두 날짜 컬럼 값 사이의 월 수를 갖는 신규 컬럼을 추가하는 방법을 보여준다. ▶

[PYTHON/PANDAS] MonthBegin 클래스 : DataFrame 객체에서 날짜 컬럼 값 기준 다음 달 시작일을 갖는 신규 컬럼 추가하기

■ MonthBegin 클래스를 사용해 DataFrame 객체에서 날짜 컬럼의 값을 기준으로 다음 달 시작일을 갖는 신규 컬럼을 추가하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

dataFrame["date1"] = pd.Timestamp("2013-01-15")

dataFrame["date1_next" ] = dataFrame["date1"] + pd.offsets.MonthBegin()

print(dataFrame)

"""
     total_bill   tip     sex smoker   day    time  size      date1 date1_next
0         16.99  1.01  Female     No   Sun  Dinner     2 2013-01-15 2013-02-01
1         10.34  1.66    Male     No   Sun  Dinner     3 2013-01-15 2013-02-01
2         21.01  3.50    Male     No   Sun  Dinner     3 2013-01-15 2013-02-01
3         23.68  3.31    Male     No   Sun  Dinner     2 2013-01-15 2013-02-01
4         24.59  3.61  Female     No   Sun  Dinner     4 2013-01-15 2013-02-01
..          ...   ...     ...    ...   ...     ...   ...        ...        ...
239       29.03  5.92    Male     No   Sat  Dinner     3 2013-01-15 2013-02-01
240       27.18  2.00  Female    Yes   Sat  Dinner     2 2013-01-15 2013-02-01
241       22.67  2.00    Male    Yes   Sat  Dinner     2 2013-01-15 2013-02-01
242       17.82  1.75    Male     No   Sat  Dinner     2 2013-01-15 2013-02-01
243       18.78  3.00  Female     No  Thur  Dinner     2 2013-01-15 2013-02-01

[244 rows x 9 columns]
"""

import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

dataFrame["date1"] = pd.Timestamp("2013-01-15")

dataFrame["date1_next" ] = dataFrame["date1"] + pd.offsets.MonthBegin()

print(dataFrame)

"""

total_bill tip sex smoker day time size date1 date1_next

0 16.99 1.01 Female No Sun Dinner 2 2013-01-15 2013-02-01

1 10.34 1.66 Male No Sun Dinner 3 2013-01-15 2013-02-01

2 21.01 3.50 Male No Sun Dinner 3 2013-01-15 2013-02-01

3 23.68 3.31 Male No Sun Dinner 2 2013-01-15 2013-02-01

4 24.59 3.61 Female No Sun Dinner 4 2013-01-15 2013-02-01

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

239 29.03 5.92 Male No Sat Dinner 3 2013-01-15 2013-02-01

240 27.18 2.00 Female Yes Sat Dinner 2 2013-01-15 2013-02-01

241 22.67 2.00 Male Yes Sat Dinner 2 2013-01-15 2013-02-01

242 17.82 1.75 Male No Sat Dinner 2 2013-01-15 2013-02-01

243 18.78 3.00 Female No Thur Dinner 2 2013-01-15 2013-02-01

[244 rows x 9 columns]

"""

[PYTHON/PANDAS] DatetimeProperties 클래스 : year/month/day 속성을 사용해 DataFrame 객체 컬럼 값 설정하기

■ DatetimeProperties 클래스의 year/month/day 속성을 사용해 DataFrame 객체 컬럼의 값을 설정하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

dataFrame["date1"] = pd.Timestamp("2013-01-15")

datetimeProperties = dataFrame["date1"].dt

dataFrame["date1_year" ] = datetimeProperties.year
dataFrame["date1_month"] = datetimeProperties.month
dataFrame["date1_day"  ] = datetimeProperties.day

print(dataFrame)

"""
     total_bill   tip     sex smoker   day    time  size      date1  date1_year  date1_month  date1_day
0         16.99  1.01  Female     No   Sun  Dinner     2 2013-01-15        2013            1         15
1         10.34  1.66    Male     No   Sun  Dinner     3 2013-01-15        2013            1         15
2         21.01  3.50    Male     No   Sun  Dinner     3 2013-01-15        2013            1         15
3         23.68  3.31    Male     No   Sun  Dinner     2 2013-01-15        2013            1         15
4         24.59  3.61  Female     No   Sun  Dinner     4 2013-01-15        2013            1         15
..          ...   ...     ...    ...   ...     ...   ...        ...         ...          ...        ...
239       29.03  5.92    Male     No   Sat  Dinner     3 2013-01-15        2013            1         15
240       27.18  2.00  Female    Yes   Sat  Dinner     2 2013-01-15        2013            1         15
241       22.67  2.00    Male    Yes   Sat  Dinner     2 2013-01-15        2013            1         15
242       17.82  1.75    Male     No   Sat  Dinner     2 2013-01-15        2013            1         15
243       18.78  3.00  Female     No  Thur  Dinner     2 2013-01-15        2013            1         15

[244 rows x 11 columns]
"""

import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

dataFrame["date1"] = pd.Timestamp("2013-01-15")

datetimeProperties = dataFrame["date1"].dt

dataFrame["date1_year" ] = datetimeProperties.year

dataFrame["date1_month"] = datetimeProperties.month

dataFrame["date1_day" ] = datetimeProperties.day

print(dataFrame)

"""

total_bill tip sex smoker day time size date1 date1_year date1_month date1_day

0 16.99 1.01 Female No Sun Dinner 2 2013-01-15 2013 1 15

1 10.34 1.66 Male No Sun Dinner 3 2013-01-15 2013 1 15

2 21.01 3.50 Male No Sun Dinner 3 2013-01-15 2013 1 15

3 23.68 3.31 Male No Sun Dinner 2 2013-01-15 2013 1 15

4 24.59 3.61 Female No Sun Dinner 4 2013-01-15 2013 1 15

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

239 29.03 5.92 Male No Sat Dinner 3 2013-01-15 2013 1 15

240 27.18 2.00 Female Yes Sat Dinner 2 2013-01-15 2013 1 15

241 22.67 2.00 Male Yes Sat Dinner 2 2013-01-15 2013 1 15

242 17.82 1.75 Male No Sat Dinner 2 2013-01-15 2013 1 15

243 18.78 3.00 Female No Thur Dinner 2 2013-01-15 2013 1 15

[244 rows x 11 columns]

"""

▶ requirements.txt


defusedxml==0.7.1
et_xmlfile==2.0.0
numpy==2.1.3
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

defusedxml==0.7.1

et_xmlfile==2.0.0

numpy==2.1.3

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

[PYTHON/PANDAS] where 함수 : 특정 조건에 따라 값을 설정하는 DataFrame 객체 신규 컬럼 추가하기

■ where 함수를 사용해 특정 조건에 따라 값을 설정하는 DataFrame 객체의 신규 컬럼을 추가하는 방법을 보여준다. ▶ main.py


import pandas as pd
import numpy  as np

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

dataFrame["bucket"] = np.where(dataFrame["total_bill"] < 10, "low", "high")

print(dataFrame)

"""
     total_bill   tip     sex smoker   day    time  size bucket
0         16.99  1.01  Female     No   Sun  Dinner     2   high
1         10.34  1.66    Male     No   Sun  Dinner     3   high
2         21.01  3.50    Male     No   Sun  Dinner     3   high
3         23.68  3.31    Male     No   Sun  Dinner     2   high
4         24.59  3.61  Female     No   Sun  Dinner     4   high
..          ...   ...     ...    ...   ...     ...   ...    ...
239       29.03  5.92    Male     No   Sat  Dinner     3   high
240       27.18  2.00  Female    Yes   Sat  Dinner     2   high
241       22.67  2.00    Male    Yes   Sat  Dinner     2   high
242       17.82  1.75    Male     No   Sat  Dinner     2   high
243       18.78  3.00  Female     No  Thur  Dinner     2   high

[244 rows x 8 columns]
"""

import pandas as pd

import numpy as np

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

dataFrame["bucket"] = np.where(dataFrame["total_bill"] < 10, "low", "high")

print(dataFrame)

"""

total_bill tip sex smoker day time size bucket

0 16.99 1.01 Female No Sun Dinner 2 high

1 10.34 1.66 Male No Sun Dinner 3 high

2 21.01 3.50 Male No Sun Dinner 3 high

3 23.68 3.31 Male No Sun Dinner 2 high

4 24.59 3.61 Female No Sun Dinner 4 high

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

239 29.03 5.92 Male No Sat Dinner 3 high

240 27.18 2.00 Female Yes Sat Dinner 2 high

241 22.67 2.00 Male Yes Sat Dinner 2 high

242 17.82 1.75 Male No Sat Dinner 2 high

243 18.78 3.00 Female No Thur Dinner 2 high

[244 rows x 8 columns]

"""

▶ requirements.txt


defusedxml==0.7.1
et_xmlfile==2.0.0
numpy==2.1.3
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

defusedxml==0.7.1

et_xmlfile==2.0.0

numpy==2.1.3

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

[PYTHON/PANDAS] read_excel 함수 : index_col 인자를 사용해 엑셀 파일 데이터 로드하기

■ read_excel 함수의 index_col 인자를 사용해 엑셀 파일 데이터를 로드하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

print(dataFrame)

"""
     total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]
"""

import pandas as pd

dataFrame = pd.read_excel("tips.xlsx", index_col = 0)

print(dataFrame)

"""

total_bill tip sex smoker day time size

0 16.99 1.01 Female No Sun Dinner 2

1 10.34 1.66 Male No Sun Dinner 3

2 21.01 3.50 Male No Sun Dinner 3

3 23.68 3.31 Male No Sun Dinner 2

4 24.59 3.61 Female No Sun Dinner 4

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

239 29.03 5.92 Male No Sat Dinner 3

240 27.18 2.00 Female Yes Sat Dinner 2

241 22.67 2.00 Male Yes Sat Dinner 2

242 17.82 1.75 Male No Sat Dinner 2

243 18.78 3.00 Female No Thur Dinner 2

[244 rows x 7 columns]

"""

▶ requirements.txt


defusedxml==0.7.1
et_xmlfile==2.0.0
numpy==2.1.3
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

defusedxml==0.7.1

et_xmlfile==2.0.0

numpy==2.1.3

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]"

[PYTHON/PANDAS] DataFrame 클래스 : to_excel 메소드를 사용해 엑셀 파일 생성하기

■ DataFrame 클래스의 to_excel 메소드를 사용해 엑셀 파일을 생성하는 방법을 보여준다. ▶ main.py


import pandas as pd

dataFrame = pd.read_table("tips.csv", sep = ",", header = [0])

dataFrame.to_excel("tips.xlsx")

import pandas as pd

dataFrame = pd.read_table("tips.csv", sep = ",", header = [0])

dataFrame.to_excel("tips.xlsx")

▶ requirements.txt


defusedxml==0.7.1
et_xmlfile==2.0.0
numpy==2.1.3
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

defusedxml==0.7.1

et_xmlfile==2.0.0

numpy==2.1.3

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

[PYTHON/PANDAS] DataFrame 클래스 : iloc 속성을 사용해 값 설정하기

■ DataFrame 클래스의 iloc 속성을 사용해 값을 설정하는 방법을 보여준다. ▶ main.py


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]
"""

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

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.

[PYTHON/PANDAS] DataFrame 클래스 : iloc 속성을 사용해 부분 데이터 DataFrame 객체 구하기

■ DataFrame 클래스의 iloc 속성을 사용해 부분 데이터 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py


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
"""

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

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

[PYTHON/PANDAS] DataFrame 클래스 : loc 속성을 사용해 특정 조건을 만족하는 특정 컬럼 Series 객체 구하기

■ DataFrame 클래스의 loc 속성을 사용해 특정 조건을 만족하는 특정 컬럼 Series 객체를 구하는 방법을 보여준다. ▶ main.py


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
"""

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

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

[PYTHON/PANDAS] DataFrame 클래스 : [] 연산자를 사용해 특정 컬럼에서 값을 갖는 DataFrame 객체 구하기

■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼에서 값을 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py


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]
"""

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

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

※

[PYTHON/PANDAS] DataFrame 클래스 : [] 연산자를 사용해 특정 컬럼에서 값을 갖는 DataFrame 객체 구하기

■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼에서 값을 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py


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]
"""

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

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

※

[PYTHON/PANDAS] Series 클래스 : notna 메소드를 사용해 값 여부 Series 객체 구하기

■ Series 클래스의 notna 메소드를 사용해 값 여부 Series 객체를 구하는 방법을 보여준다. ▶ main.py


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
"""

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

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

[PYTHON/PANDAS] DataFrame 클래스 : [] 연산자를 사용해 특정 컬럼에서 특정 값들을 갖는 DataFrame 객체 구하기 2

■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼에서 특정 값들을 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py


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]
"""

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

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

[PYTHON/PANDAS] DataFrame 클래스 : [] 연산자를 사용해 특정 컬럼에서 특정 값들을 갖는 DataFrame 객체 구하기 1

■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼에서 특정 값들을 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py


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]
"""

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

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

[PYTHON/PANDAS] Series 클래스 : isin 메소드를 사용해 지정 값 리스트 포함 여부 Series 객체 구하기 1

■ Series 클래스의 isin 메소드를 사용해 지정 값 리스트에 포함 여부를 갖는 Series 객체를 구하는 방법을 보여준다. ▶ main.py


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
"""

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

[PYTHON/PANDAS] DataFrame 클래스 : 특정 조건에 해당하는 데이터를 갖는 DataFrame 객체 구하기

■ DataFrame 클래스에서 특정 조건에 해당하는 데이터를 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py


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]
"""

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

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

[PYTHON/PANDAS] DataFrame 클래스 : [] 연산자를 사용해 특정 컬럼들의 데이터를 갖는 DataFrame 객체 구하기

■ DataFrame 클래스에서 [] 연산자를 사용해 특정 컬럼들의 데이터를 갖는 DataFrame 객체를 구하는 방법을 보여준다. ▶ main.py


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]
"""

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

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

※

[PYTHON/PANDAS] Series 클래스 : shape 속성을 사용해 데이터 차원 구하기

■ 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,)
"""

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

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