pandas 常用清洗数据（二）

1、

df.head()

Here we import pandas using the alias 'pd', then we read in our data.

df.head - shows us the first 5 rows and headers - it gives us an idea what to expect. df.tail - shows us the last 5 rows

2、

n [1]: df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
   ...:                     'B': ['B0', 'B1', 'B2', 'B3'],
   ...:                     'C': ['C0', 'C1', 'C2', 'C3'],
   ...:                     'D': ['D0', 'D1', 'D2', 'D3']},
   ...:                     index=[0, 1, 2, 3])
   ...: `

In [2]: df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
   ...:                     'B': ['B4', 'B5', 'B6', 'B7'],
   ...:                     'C': ['C4', 'C5', 'C6', 'C7'],
   ...:                     'D': ['D4', 'D5', 'D6', 'D7']},
   ...:                      index=[4, 5, 6, 7])
   ...:

In [3]: df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],
   ...:                     'B': ['B8', 'B9', 'B10', 'B11'],
   ...:                     'C': ['C8', 'C9', 'C10', 'C11'],
   ...:                     'D': ['D8', 'D9', 'D10', 'D11']},
   ...:                     index=[8, 9, 10, 11])

　　

In frames = [df1, df2, df3]
In [5]: result = pd.concat(frames)
result = df1.append([df2, df3])

2、copy and value_counts

df2 = df.copy()
df2.DATE.value_counts().sort_index() //sort by index

data_print = data['vote_count'].value_counts().sort_index()

df['Amount'] = pd.to_numeric(df['Amount'])

3, add  del

del df2['column_name'] 
del df2['column_name'] 
del df2['column_name']

df.insert(loc=0, column='Country', value='UK')
data.insert(0, '性别', data.pop('gender'))#pop返回删除的列，插入到第0列，并取新名为'性别'

4、筛选：

  

bool = dt.str.contains   # df 是Series类型，不是DataFrame类型
                                   #返回的是True，False
#获取筛选数据
xuan_data =dt[bool]  #True的