DataFrame.dropna

DataFrame.dropna(axis=0, how=’any’, thresh=None, subset=None, inplace=False)
于删除缺失数据。

Parameters

axis 0或index：删除包含缺失的行；1或columns：删除包含缺失值的列
how any：如果存在NA值，删除该行或列；all：如果所有值都是NA，删除该行或列
thresh 需要的非NA值个数
subset 删除行，需要包括的列的列表；删除列，需要包括的行的列表
inplace False：返回副本；True：就地执行操作并返回None

Example

  1. import pandas as pd
  3. df = pd.DataFrame({'site':['google', 'baidu', 'wiki'],
  4.                     'age':[18, 39, 22],
  5.                     'price': [None, 2.0, 3.0],
  6.                     'color': [None, 'black', None]})
  7. df.dropna()
  8. -------------------------------------
  9.     site    age    price    color
  10. 1    baidu    39    2.0        black

Example

  1. import pandas as pd
  3. df = pd.DataFrame({'site':['google', 'baidu', 'wiki'],
  4.                     'age':[18, 39, 22],
  5.                     'price': [None, 2.0, 3.0],
  6.                     'color': [None, 'black', None]})
  8. df.dropna(axis='columns')
  9. ---------------------------------------
  10.     site    age
  11. 0    google    18
  12. 1    baidu    39
  13. 2    wiki    22

Example

  1. import pandas as pd
  3. df = pd.DataFrame({'site':[None, 'baidu', 'wiki'],
  4.                     'age':[None, 39, 22],
  5.                     'price': [None, 2.0, 3.0],
  6.                     'color': [None, 'black', None]})
  8. df.dropna(how='all')
  9. ---------------------------------------
  10.     site    age        price    color
  11. 1    baidu    39.0    2.0        black
  12. 2    wiki    22.0    3.0        None

Example

  1. import pandas as pd
  3. df = pd.DataFrame({'site':['google', 'baidu', 'wiki'],
  4.                     'age':[18, 39, 22],
  5.                     'price': [None, 2.0, 3.0],
  6.                     'color': [None, 'black', None]})
  8. df.dropna(thresh=3)
  9. -------------------------------------
  10.     site    age        price    color
  11. 1    baidu    39        2.0        black
  12. 2    wiki    22        3.0        None

Example

  1. import pandas as pd
  3. df = pd.DataFrame({'site':['google', 'baidu', 'wiki'],
  4.                     'age':[18, 39, 22],
  5.                     'price': [None, 2.0, 3.0],
  6.                     'color': [None, 'black', None]})
  8. df.dropna(subset=['age', 'price'])
  9. --------------------------------------------
  10.     site    age        price    color
  11. 1    baidu    39        2.0        black
  12. 2    wiki    22        3.0        None