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 pd
import 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  size
0       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    time
0       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  size
0       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 col2
0    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 col2
1    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 col2
0    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

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