鍦≒ython涓紝鍙互浣跨敤鍚勭搴撳拰宸ュ叿鏉ユ竻娲楁暟鎹€備笅闈㈡槸涓€浜涘父鐢ㄧ殑鏂规硶锛?/p>

1. 鏁版嵁鍘婚噸锛氫娇鐢╬andas搴撶殑drop_duplicates()鍑芥暟鍙互鍘婚櫎閲嶅鐨勬暟鎹銆?/li>

import pandas as pd
df = pd.DataFrame({'col1': ['A', 'B', 'A', 'C', 'B'],
'col2': [1, 2, 3, 4, 5]})
df.drop_duplicates()

2. 缂哄け鍊煎鐞嗭細浣跨敤pandas搴撶殑fillna()鍑芥暟鍙互濉厖缂哄け鍊硷紝浣跨敤dropna()鍑芥暟鍙互鍒犻櫎鍚湁缂哄け鍊肩殑琛屻€?/li>

import pandas as pd
df = pd.DataFrame({'col1': [1, 2, None, 4],
'col2': [None, 2, 3, 4]})
df.fillna(0)  # 濉厖缂哄け鍊间负0
df.dropna()  # 鍒犻櫎鍚湁缂哄け鍊肩殑琛?/span>

3. 鏁版嵁杞崲锛氫娇鐢╬andas搴撶殑apply()鍑芥暟鍙互瀵规暟鎹繘琛岃浆鎹紝閫氳繃鑷畾涔夌殑鍑芥暟鍙互瀹炵幇鍚勭鏁版嵁娓呮礂鎿嶄綔銆?/li>

import pandas as pd
df = pd.DataFrame({'col1': ['a', 'b', 'c', 'd'],
'col2': [1, 2, 3, 4]})
def convert_to_uppercase(x):
return x.upper()
df['col1'] = df['col1'].apply(convert_to_uppercase)  # 灏哻ol1鍒楃殑鍊艰浆鎹负澶у啓

4. 鏁版嵁鏍煎紡杞崲锛氫娇鐢╬andas搴撶殑astype()鍑芥暟鍙互灏嗘暟鎹殑绫诲瀷杞崲涓烘寚瀹氱殑鏍煎紡銆?/li>

import pandas as pd
df = pd.DataFrame({'col1': [1, 2, 3, 4],
'col2': [1.1, 2.2, 3.3, 4.4]})
df['col2'] = df['col2'].astype(int)  # 灏哻ol2鍒楃殑鍊艰浆鎹负鏁村瀷

5. 鏁版嵁鏍囧噯鍖栵細浣跨敤sklearn搴撶殑StandardScaler绫诲彲浠ュ鏁版嵁杩涜鏍囧噯鍖栧鐞嗐€?/li>

from sklearn.preprocessing import StandardScaler
data = [[1, 2], [3, 4], [5, 6]]
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)  # 瀵规暟鎹繘琛屾爣鍑嗗寲澶勭悊

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