本文所整理的技巧与以前整理过10个Pandas的常用技巧不同，你可能并不会经常的使用它，但是有时候当你遇到一些非常棘手的问题时，这些技巧可以帮你快速解决一些不常见的问题。

1、Categorical类型

 # raw data +----------+------------------------+ |  class   |        filename        | +----------+------------------------+ | Bathroom | Bathroom\bath_1.jpg    | | Bathroom | Bathroom\bath_10.jpg  | | Bathroom | Bathroom\bath_1003.jpg | | Bathroom | Bathroom\bath_1004.jpg | | Bathroom | Bathroom\bath_1005.jpg | +----------+------------------------+
 # target +------------------------+------------------------+----------------------------+ |         anchor         |        positive        |          negative          | +------------------------+------------------------+----------------------------+ | Bathroom\bath_1.jpg    | Bathroom\bath_10.jpg  | Dinning\din_540.jpg        | | Bathroom\bath_100.jpg  | Bathroom\bath_1003.jpg | Dinning\din_1593.jpg       | | Bathroom\bath_1003.jpg | Bathroom\bath_1004.jpg | Bedroom\bed_329.jpg        | | Bathroom\bath_1004.jpg | Bathroom\bath_1005.jpg | Livingroom\living_1030.jpg | | Bathroom\bath_1005.jpg | Bathroom\bath_1007.jpg | Bedroom\bed_1240.jpg       | +------------------------+------------------------+----------------------------+

 triplets.info(memory_usage="deep")
 #   Column   Non-Null Count   Dtype   # --- ------   --------------   -----   # 0   anchor   525000 non-null category # 1   positive 525000 non-null category # 2   negative 525000 non-null category # d*: category(3) # memory usage: 4.6 MB
 # without categories triplets_raw.info(memory_usage="deep")
 #   Column   Non-Null Count   Dtype # --- ------   --------------   ----- # 0   anchor   525000 non-null object # 1   positive 525000 non-null object # 2   negative 525000 non-null object # d*: object(3) # memory usage: 118.1 MB

2、行列转换

 cols = sorted([col for col in original_df.columns \               if col.startswith("pct_bb")]) df = original_df[(["cfips"] + cols)] df = df.melt(id_vars="cfips",              value_vars=cols,              var_name="year",              value_name="feature").sort_values(by=["cfips", "year"])

3、apply()很慢

 import pandas as pd import swifter
 def target_function(row):     return row * 10
 def traditional_way(data):     data['out'] = data['in'].apply(target_function)
 def swifter_way(data):     data['out'] = data['in'].swifter.apply(target_function)

 import pandas as pd from pandarallel import pandarallel
 def target_function(row):     return row * 10
 def traditional_way(data):     data['out'] = data['in'].apply(target_function)
 def pandarallel_way(data):     pandarallel.initialize()     data['out'] = data['in'].parallel_apply(target_function)

4、空值，int, Int64

5、Csv, 压缩还是parquet?

 |        file            |  size   | +------------------------+---------+ | triplets_525k.csv      | 38.4 MB | | triplets_525k.csv.gzip |  4.3 MB | | triplets_525k.csv.zip  |  4.5 MB | | triplets_525k.parquet  |  1.9 MB | +------------------------+---------+

6、value_counts ()

 df = pd.DataFrame({"a": [1, 2, None], "b": [4., 5.1, 14.02]}) df["a"] = df["a"].astype("Int64") print(df.info()) print(df["a"].value_counts(normalize=True, dropna=False),      df["a"].value_counts(normalize=True, dropna=True), sep="\n\n")

7、Modin

 !pip install modin[all]
 import modin.pandas as pd df = pd.read_csv("my_dataset.csv")

8、extract()

 import pandas as pd
 regex = (r'(?P<title>[A-Za-z\'\s]+),'          r'(?P<author>[A-Za-z\s\']+),'          r'(?P<isbn>[\d-]+),'          r'(?P<year>\d{4}),'          r'(?P<publisher>.+)') addr = pd.Series([     "The Lost City of Amara,Olivia Garcia,978-1-234567-89-0,2023,HarperCollins",     "The Alchemist's Daughter,Maxwell Greene,978-0-987654-32-1,2022,Penguin Random House",     "The Last Voyage of the HMS Endeavour,Jessica Kim,978-5-432109-87-6,2021,Simon & Schuster",     "The Ghosts of Summer House,Isabella Lee,978-3-456789-12-3,2000,Macmillan Publishers",     "The Secret of the Blackthorn Manor,Emma Chen,978-9-876543-21-0,2023,Random House Children's Books"  ]) addr.str.extract(regex)

9、读写剪贴板

10、数组列分成多列

 import pandas as pd df = pd.DataFrame({"a": [1, 2, 3],              "b": [4, 5, 6],              "category": [["foo", "bar"], ["foo"], ["qux"]]})
 # let's increase the number of rows in a dataframe df = pd.concat([df]*10000, ignore_index=True)

 def dummies_series_apply(df):    return df.join(df['category'].apply(pd.Series) \                                  .stack() \                                  .str.get_dummies() \                                  .groupby(level=) \                                  .sum()) \              .drop("category", axis=1) %timeit dummies_series_apply(df.copy()) #5.96 s ± 66.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

 from sklearn.preprocessing import MultiLabelBinarizer def sklearn_mlb(df):    mlb = MultiLabelBinarizer()    return df.join(pd.DataFrame(mlb.fit_transform(df['category']), columns=mlb.classes_)) \              .drop("category", axis=1) %timeit sklearn_mlb(df.copy()) #35.1 ms ± 1.31 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

 def dummies_vectorized(df):    return pd.get_dummies(df.explode("category"), prefix="cat") \              .groupby(["a", "b"]) \              .sum() \              .reset_index() %timeit dummies_vectorized(df.copy()) #29.3 ms ± 1.22 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

总结