Pandas:1.创建、读和写操作-优快云博客

本文链接：https://blog.youkuaiyun.com/liuhehe123/article/details/85724784

本文介绍如何使用Python的Pandas库进行DataFrame的创建和数据操作，包括从列表、字典创建DataFrame，读取CSV文件，以及将数据写入文件的方法。通过实例展示了不同场景下DataFrame的构建方式。

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一、创建DataFrame

1、创建表格DataFrame

fruits = pd.DataFrame([[30, 21]], columns=['Apples', 'Bananas'])

效果：

2、再建一个不一样的DataFrame

fruit_sales = pd.DataFrame([[35, 21], [41, 34]], columns=['Apples', 'Bananas'],
                index=['2017 Sales', '2018 Sales'])

3、Series 两个列单独建

quantities = ['4 cups', '1 cup', '2 large', '1 can']
items = ['Flour', 'Milk', 'Eggs', 'Spam']
recipe = pd.Series(quantities, index=items, name='Dinner')

Flour 4 cups
Milk 1 cup
Eggs 2 large
Spam 1 can
Name: Dinner, dtype: object

二、读操作

1、读一个csv文件

reviews = pd.read_csv('../input/wine-reviews/winemag-data_first150k.csv',index_col=0)
reviews

index_col=0 这个是索引列如果指定index_col=1那么就会按country索引

下面是没有index_col的情况：

Unnamed: 0	country	description	designation	points	price	province	region_1	region_2	variety	winery
0	0	US	This tremendous 100% varietal wine hails from ...	Martha's Vineyard	96	235.0	California	Napa Valley	Napa	Cabernet Sauvignon	Heitz
1	1	Spain	Ripe aromas of fig, blackberry and cassis are ...	Carodorum Selección Especial Reserva	96	110.0	Northern Spain	Toro	NaN	Tinta de Toro	Bodega Carmen Rodríguez
...	...	...	...	...	...	...	...	...	...	...	...
150928	150928	France	A perfect salmon shade, with scents of peaches...	Grand Brut Rosé	90	52.0	Champagne	Champagne	NaN	Champagne Blend	Gosset
150929	150929	Italy	More Pinot Grigios should taste like this. A r...	NaN	90	15.0	Northeastern Italy	Alto Adige	NaN	Pinot Grigio	Alois Lageder

150930 rows × 11 columns

2、以字典方式创建一个 DataFrame 按列创建

animals = pd.DataFrame({'Cows': [12, 20], 'Goats': [22, 19]}, index=['Year 1', 'Year 2'])
animals

Year 1	12	22
Year 2	20	19

3、读sql

import sqlite3
conn = sqlite3.connect("../input/pitchfork-data/database.sqlite")
music_reviews = pd.read_sql_query("SELECT * FROM artists", conn)

三、写操作

1、将之前的文件存进硬盘

animals.to_csv("cows_and_goats.csv")

to_excel
to_pickle
...

注明：

以上内容来自kaggle