select * from table where column_name = some_value;

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
          'B': 'one one two three two two one three'.split(),
          'C': np.arange(8), 'D': np.arange(8) * 2})

df[df['A'] == 'foo'] # 判断等式是否成立

mask = df['A'] == 'foo'
pos = np.flatnonzero(mask) # 返回的是array([0, 2, 4, 6, 7])
df.iloc[pos]

#常见的iloc用法
df.iloc[:3,1:3]

df.set_index('A', append=True, drop=False).xs('foo', level=1) # xs方法适用于多重索引DataFrame的数据筛选

# 更直观点的做法
df.index=df['A'] # 将A列作为DataFrame的行索引
df.loc['foo', :]

# 使用布尔
df.loc[df['A']=='foo']

df.query('A=="foo"')

# 多条件
df.query('A=="foo" | A=="bar"')

df.loc[df['column_name'] == some_value]

df.loc[df['column_name'].isin(some_values)] # some_values是可迭代对象

df.loc[(df['column_name'] >= A) & (df['column_name'] <= B)]

df.loc[df['column_name'] != 'some_value']

df.loc[~df['column_name'].isin('some_values')] #~取反