柚子快報邀請碼778899分享：【pandas庫】常用函數(shù)總結(jié)

http://yzkb.51969.com/

文章目錄

1、pd.read_csv()2、Dataframe.drop()3、pd.get_dummies()

pandas官方文檔：https://pandas.pydata.org/pandas-docs/stable/index.html

1、pd.read_csv()

pd.read_csv()是用于讀取 CSV（Comma Separated Values，逗號分隔值）文件并將其轉(zhuǎn)換為 DataFrame 對象。CSV 是一種常見的數(shù)據(jù)存儲格式，其中數(shù)據(jù)以純文本形式存儲，每行表示一條記錄，每個字段之間用逗號（或其他分隔符）分隔。簡單使用：

pd.read_csv(file_path, sep)

1) file_path: 文件路徑

2) sep: csv文件的分隔符，默認為逗號

更復(fù)雜的使用方法：詳見https://blog.csdn.net/weixin_47139649/article/details/126744842

read_csv(

reader: FilePathOrBuffer, *,

sep: str = ...,

delimiter: str | None = ...,

header: int | Sequence[int] | str = ...,

names: Sequence[str] | None = ...,

index_col: int | str | Sequence | Literal[False] | None = ...,

usecols: int | str | Sequence | None = ...,

squeeze: bool = ...,

prefix: str | None = ...,

mangle_dupe_cols: bool = ...,

dtype: str | Mapping[str, Any] | None = ...,

engine: str | None = ...,

converters: Mapping[int | str, (*args, **kwargs) -> Any] | None = ...,

true_values: Sequence[Scalar] | None = ...,

false_values: Sequence[Scalar] | None = ...,

skipinitialspace: bool = ...,

skiprows: Sequence | int | (*args, **kwargs) -> Any | None = ...,

skipfooter: int = ..., nrows: int | None = ..., na_values=...,

keep_default_na: bool = ..., na_filter: bool = ...,

verbose: bool = ..., skip_blank_lines: bool = ...,

parse_dates: bool | List[int] | List[str] = ...,

infer_datetime_format: bool = ...,

keep_date_col: bool = ...,

date_parser: (*args, **kwargs) -> Any | None = ...,

dayfirst: bool = ..., cache_dates: bool = ...,

iterator: Literal[True],

chunksize: int | None = ...,

compression: str | None = ...,

thousands: str | None = ...,

decimal: str | None = ...,

lineterminator: str | None = ...,

quotechar: str = ...,

quoting: int = ...,

doublequote: bool = ...,

escapechar: str | None = ...,

comment: str | None = ...,

encoding: str | None = ...,

dialect: str | None = ...,

error_bad_lines: bool = ...,

warn_bad_lines: bool = ...,

delim_whitespace: bool = ...,

low_memory: bool = ...,

memory_map: bool = ...,

float_precision: str | None = ...)

2、Dataframe.drop()

用于刪除 DataFrame 或 Series 中的指定行、列或元素。

DataFrame.drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors=‘raise’)

1) labels: 指定要刪除的列名或者行索引，可以是單個值(int/str)或者list

2) axis: 指定刪除方向(行或列)，0 或 ‘index’ ： 刪除行；1 or ‘columns’： 刪除列

3) index: 用于指定要刪除的行索引(index=labels 等效于 labels, axis=0)

4) columns: 用于指定要刪除的列名(columns=labels 等效于 labels, axis=1)

5) inplace: bool類型，True表示原地修改，F(xiàn)alse表示返回一個新的DataFrame，默認為False

例如：

import pandas as pd

# 創(chuàng)建一個簡單的 DataFrame

df = pd.DataFrame({

'A': [1, 2, 3],

'B': [4, 5, 6],

'C': [7, 8, 9]

})

# 刪除列 'A'

df_dropped = df.drop('A', axis=1)

# 這與下面的用法是等效的

df_dropped_equiv = df.drop(columns='A')

# 刪除索引為 1 的行

df_dropped_row = df.drop(1, axis=0)

# 這與下面的用法是等效的

df_dropped_row_equiv = df.drop(index=1)

3、pd.get_dummies()

pd.get_dummies()是將類別變量轉(zhuǎn)換為one-hot變量，進行one-hot編碼，一般用于數(shù)據(jù)的預(yù)處理，在推薦系統(tǒng)中將類別變量轉(zhuǎn)換為one-hot變量后，可繼續(xù)進行embedding

pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False)[source]

1) data: 待轉(zhuǎn)換的類別變量，可以是Series, or DataFrame

2) prefix: str類型，是生成的新列的前綴，可見如下例子

例如：

import pandas as pd

data = pd.DataFrame({

'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],

'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],

'C': np.random.randn(8),

'D': np.random.randn(8)

})

dummy_data = pd.get_dummies(data['A'], prefix='A')

'''

結(jié)果 dummy_data 將是：

A_bar A_foo

0 0 1

1 1 0

2 0 1

3 1 0

4 0 1

5 1 0

6 0 1

7 0 1

'''

柚子快報邀請碼778899分享：【pandas庫】常用函數(shù)總結(jié)

http://yzkb.51969.com/

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