如何在 Python 中使用 Pandas 取得所有重複項的清單
在處理資料集時,經常會遇到重複的條目。在這種情況下,您希望使用 Pandas 識別資料集中的所有重複項。
要實現此目的,您可以使用以下方法:
方法1(使用以下命令列印所有行)重複ID):
<code class="python">import pandas as pd # Read the CSV data into a DataFrame df = pd.read_csv("dup.csv") # Extract the "ID" column ids = df["ID"] # Create a new DataFrame with only the duplicate values duplicates = df[ids.isin(ids[ids.duplicated()])] # Sort the DataFrame by the "ID" column duplicates.sort_values("ID", inplace=True) # Print the duplicate values print(duplicates)</code>
方法2(分組並連接重複組):
此方法組合重複組,從而得到簡潔的表示重複項目的數量:
<code class="python"># Group the DataFrame by the "ID" column grouped = df.groupby("ID") # Filter the grouped DataFrame to include only groups with more than one row duplicates = [g for _, g in grouped if len(g) > 1] # Concatenate the duplicate groups into a new DataFrame duplicates = pd.concat(duplicates) # Print the duplicate values print(duplicates)</code>
使用方法1 或方法2,您可以成功取得資料集中所有重複項目的列表,以便您直觀地檢查它們並調查差異。
以上是如何在 Python 中辨識和檢索 Pandas DataFrame 中的重複項?的詳細內容。更多資訊請關注PHP中文網其他相關文章!

ForhandlinglargedatasetsinPython,useNumPyarraysforbetterperformance.1)NumPyarraysarememory-efficientandfasterfornumericaloperations.2)Avoidunnecessarytypeconversions.3)Leveragevectorizationforreducedtimecomplexity.4)Managememoryusagewithefficientdata

Inpython,ListSusedynamicMemoryAllocationWithOver-Asalose,而alenumpyArraySallaySallocateFixedMemory.1)listssallocatemoremoremoremorythanneededinentientary上,respizeTized.2)numpyarsallaysallaysallocateAllocateAllocateAlcocateExactMemoryForements,OfferingPrediCtableSageButlessemageButlesseflextlessibility。

Inpython,YouCansspecthedatatAtatatPeyFelemereModeRernSpant.1)Usenpynernrump.1)Usenpynyp.dloatp.dloatp.ploatm64,formor professisconsiscontrolatatypes。

NumPyisessentialfornumericalcomputinginPythonduetoitsspeed,memoryefficiency,andcomprehensivemathematicalfunctions.1)It'sfastbecauseitperformsoperationsinC.2)NumPyarraysaremorememory-efficientthanPythonlists.3)Itoffersawiderangeofmathematicaloperation

Contiguousmemoryallocationiscrucialforarraysbecauseitallowsforefficientandfastelementaccess.1)Itenablesconstanttimeaccess,O(1),duetodirectaddresscalculation.2)Itimprovescacheefficiencybyallowingmultipleelementfetchespercacheline.3)Itsimplifiesmemorym

SlicingaPythonlistisdoneusingthesyntaxlist[start:stop:step].Here'showitworks:1)Startistheindexofthefirstelementtoinclude.2)Stopistheindexofthefirstelementtoexclude.3)Stepistheincrementbetweenelements.It'susefulforextractingportionsoflistsandcanuseneg

numpyallowsforvariousoperationsonArrays:1)basicarithmeticlikeaddition,減法,乘法和division; 2)evationAperationssuchasmatrixmultiplication; 3)element-wiseOperations wiseOperationswithOutexpliitloops; 4)

Arresinpython,尤其是Throughnumpyandpandas,weessentialFordataAnalysis,offeringSpeedAndeffied.1)NumpyArseNable efflaysenable efficefliceHandlingAtaSetSetSetSetSetSetSetSetSetSetSetsetSetSetSetSetsopplexoperationslikemovingaverages.2)


熱AI工具

Undresser.AI Undress
人工智慧驅動的應用程序,用於創建逼真的裸體照片

AI Clothes Remover
用於從照片中去除衣服的線上人工智慧工具。

Undress AI Tool
免費脫衣圖片

Clothoff.io
AI脫衣器

Video Face Swap
使用我們完全免費的人工智慧換臉工具,輕鬆在任何影片中換臉!

熱門文章

熱工具

SublimeText3 英文版
推薦:為Win版本,支援程式碼提示!

Dreamweaver CS6
視覺化網頁開發工具

SublimeText3 Mac版
神級程式碼編輯軟體(SublimeText3)

SublimeText3 Linux新版
SublimeText3 Linux最新版

ZendStudio 13.5.1 Mac
強大的PHP整合開發環境