Python 数据分析:让你像写 Sql 语句一样,使用 Pandas 做数据分析
python-数据分析让你像写-sql-语句一样使用-pandas-做数据分析">Python
数据分析:让你像写 Sql
语句一样,使用 Pandas
做数据分析
一、加载数据
import pandas as pdimport numpy as np
url = (\'https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/tips.csv\')
tips = pd.read_csv(url)
output = tips.head()
Output:
total_bill tip sex smoker day time size0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
二、SELECT 的使用方式
sql 语句: SELECT total_bill, tip, smoker, time FROM tips LIMIT 5;
。
output = tips[[\'total_bill\', \'tip\', \'smoker\', \'time\']].head(5)
Output:
total_bill tip smoker time0 16.99 1.01 No Dinner
1 10.34 1.66 No Dinner
2 21.01 3.50 No Dinner
3 23.68 3.31 No Dinner
4 24.59 3.61 No Dinner
三、WHERE 的使用方式
1. 举个栗子
sql 语句: SELECT * FROM tips WHERE time = \'Dinner\' LIMIT 5;
output = tips[tips[\'time\'] == \'Dinner\'].head(5)# 或者
output = tips.query("time == \'Dinner\'").head(5)
Output:
total_bill tip sex smoker day time size0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
2. 比较运算符:等于 ==
、 大于 >
、 大于等于 >=
、小于等于 <=
、不等于 !=
2.1 等于 ==
sql 语句:SELECT * FROM tips WHERE time = \'Dinner\';
。
output = tips[(tips[\'time\'] == \'Dinner\')]
2.2 大于 >
sql 语句:SELECT * FROM tips WHERE tip > 5.00;
。
output = tips[(tips[\'tip\'] > 5.00)]
2.3 大于等于 >=
sql 语句:SELECT * FROM tips WHERE tip >= 5.00;
。
output = tips[(tips[\'size\'] >= 5)]
2.4 小于等于 <=
sql 语句:SELECT * FROM tips WHERE tip <= 5.00;
。
output = tips[(tips[\'size\'] <= 5)]
2.5 不等于 !=
sql 语句:SELECT * FROM tips WHERE tip <> 5.00;
。
output = tips[(tips[\'size\'] != 5)]
3. 逻辑运算符:且 &
、或 |
、非 -
3.1 且 &
sql 语句:SELECT * FROM tips WHERE time = \'Dinner\' AND tip > 5.00;
output = tips[(tips[\'time\'] == \'Dinner\') & (tips[\'tip\'] > 5.00)]
3.2 或 |
sql 语句:SELECT * FROM tips WHERE size >= 5 OR total_bill > 45;
。
output = tips[(tips[\'size\'] >= 5) | (tips[\'total_bill\'] > 45)]
3.3 非 -
sql 语句:SELECT * FROM tips WHERE not (size <> 5 AND size > 4);
output = df[-((df[\'size\'] != 5) & (df[\'size\'] > 4))]
4. Null 的判断
这里重新定义一个包含 NaN
数据的 DataFrame
。
frame = pd.DataFrame({ \'col1\': [\'A\', \'B\', np.NaN, \'C\', \'D\'],
\'col2\': [\'F\', np.NaN, \'G\', \'H\', \'I\']
})
output = frame
Output:
col1 col20 A F
1 B NaN
2 NaN G
3 C H
4 D I
4.1 判断列是 Null
sql 语句:SELECT * FROM frame WHERE col2 IS NULL;
。
output = frame[frame[\'col2\'].isna()]
Output:
col1 col21 B NaN
4.2 判断列不是 Null
sql 语句:SELECT * FROM frame WHERE col1 IS NOT NULL;
。
output = frame[frame[\'col1\'].notna()]
Output:
col1 col20 A F
1 B NaN
3 C H
4 D I
5. In、Like 操作
5.1 In
sql 语句:SELECT * FROM tips WHERE siez in (5, 6);
。
output = tips[tips[\'size\'].isin([2, 5])]
5.2 Like
sql 语句:SELECT * FROM tips WHERE time like \'Din%\';
。
output = tips[tips.time.str.contains(\'Din*\')]
四、GROUP BY 的使用方式
sql 语句:SELECT sex, count(*) FROM tips GROUP BY sex;
output = tips.groupby(\'sex\').size()# 获取相应的结果
output[\'Male\']
output[\'Female\']
output = tips.groupby(\'sex\').count()# 获取相应的结果
output[\'tip\'][\'Female\']
output = tips.groupby(\'sex\')[\'total_bill\'].count()# 获取相应的结果
output[\'Male\']
output[\'Female\']
sql 语句:SELECT day, AVG(tip), COUNT(*) FROM tips GROUP BY day;
output = tips.groupby(\'day\').agg({\'tip\': np.mean, \'day\': np.size})# 获取相应的结果
output[\'day\'][\'Fri\']
output[\'tip\'][\'Fri\']
sql 语句:SELECT smoker, day, COUNT(*), AVG(tip) FROM tips GROUP BY smoker, day;
output = tips.groupby([\'smoker\', \'day\']).agg({\'tip\': [np.size, np.mean]})# 获取相应的结果
output[\'tip\'][\'size\'][\'No\'][\'Fri\']
sql 语句:SELECT tip, count(distinct sex) FROM tips GROUP BY tip;
output = tips.groupby(\'tip\').agg({\'sex\': pd.Series.nunique})
五、JOIN 连接的使用方式
定义两个 DataFrame。
df1 = pd.DataFrame({\'key\': [\'A\', \'B\', \'C\', \'D\'], \'value\': np.random.randn(4)})df2 = pd.DataFrame({\'key\': [\'B\', \'D\', \'D\', \'E\'], \'value\': np.random.randn(4)})
1. 内连接 Inner Join
sql 语句:SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key;
output = pd.merge(df1, df2, on=\'key\')# 或
indexed_df2 = df2.set_index(\'key\')
pd.merge(df1, indexed_df2, left_on=\'key\', right_index=True)
2. 左连接 Left Outer Join
sql 语句:SELECT * FROM df1 LEFT OUTER JOIN df2 ON df1.key = df2.key;
output = pd.merge(df1, df2, on=\'key\', how=\'left\')# 或
output = df1.join(df2, on=\'key\', how=\'left\')
3. 右连接 Right Join
sql 语句:SELECT * FROM df1 RIGHT OUTER JOIN df2 ON df1.key = df2.key;
output = pd.merge(df1, df2, on=\'key\', how=\'right\')
4. 全连接 Full Join
sql 语句:SELECT * FROM df1 FULL OUTER JOIN df2 ON df1.key = df2.key;
output = pd.merge(df1, df2, on=\'key\', how=\'outer\')
五、UNION 的使用方式
df1 = pd.DataFrame({\'city\': [\'Chicago\', \'San Francisco\', \'New York City\'], \'rank\': range(1, 4)})df2 = pd.DataFrame({\'city\': [\'Chicago\', \'Boston\', \'Los Angeles\'], \'rank\': [1, 4, 5]})
sql 语句:SELECT city, rank FROM df1 UNION ALL SELECT city, rank FROM df2;
output = pd.concat([df1, df2])
sql 语句:SELECT city, rank FROM df1 UNION SELECT city, rank FROM df2;
output = pd.concat([df1, df2]).drop_duplicates()
六、与 SQL 等价的其他语法
1. 去重 Distinct
sql 语句:SELECT DISTINCT sex FROM tips;
output = tips.drop_duplicates(subset=[\'sex\'], keep=\'first\', inplace=False)
2. 修改列别名 As
sql 语句:SELECT total_bill AS total, sex AS xes FROM tips;
output = tips.rename(columns={\'total_bill\': \'total\', \'sex\': \'xes\'}, inplace=False)
3. Limit 与 Offset
sql 语句:SELECT * FROM tips ORDER BY tip DESC LIMIT 10 OFFSET 5;
output = tips.nlargest(10 + 5, columns=\'tip\').tail(10)
4. 每个 Group 的前几行
sql 语句:
SELECT * FROM ( SELECT
t.*,
ROW_NUMBER() OVER(PARTITION BY day ORDER BY total_bill DESC) AS rn
FROM tips t
)
WHERE rn < 3
ORDER BY day, rn;
output = tips.assign(rn=tips.sort_values([\'total_bill\'], ascending=False).\ groupby([\'day\']).cumcount() + 1).\
query(\'rn < 3\').\
sort_values([\'day\', \'rn\'])
七、Update 的使用方式
sql 语句:UPDATE tips SET tip = tip*2 WHERE tip < 2;
output = tips.loc[tips[\'tip\'] < 2, \'tip\'] *= 2
八、Delete 的使用方式
sql 语句:DELETE FROM tips WHERE tip > 9;
output = tips = tips.loc[tips[\'tip\'] <= 9]
九、参考文章
- 【Python实战】Pandas:让你像写SQL一样做数据分析(一)
- Comparison with SQL
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