pandas删除行删除列增加行增加列的实现

>>> df = pd.DataFrame(np.arange(16).reshape(4, 4), columns=list('ABCD'), index=list('1234'))

>>> df

  A  B  C  D

1  0  1  2  3

2  4  5  6  7

3  8  9 10 11

4 12 13 14 15 

df = df.drop(['1', '2'])      # 不指定axis默认为0

df.drop(['1', '3'], inplace=True) 

df.drop(df.index[0], inplace=True)    # 删除第1行
df.drop(df.index[0:3], inplace=True)   # 删除前3行
df.drop(df.index[[0, 2]], inplace=True) # 删除第1第3行 

>>> df.loc['2','B']=9

>>> df

  A  B  C  D

1  0  1  2  3

2  4  9  6  7

3  8  9 10 11

4 12 13 14 15

>>> chooses = df['B'].drop_duplicates().index

>>> df.loc[chooses]

  A  B  C  D

1  0  1  2  3

2  4  9  6  7

4 12 13 14 15 

del df['A'] # 删除A列，会就地修改 

df = df.drop(['B', 'C'], axis=1)        # drop不会就地修改，创建副本返回

df.drop(['B', 'C'], axis=1, inplace=True)   # inplace=True会就地修改 

df.drop(df.columns[0], axis=1, inplace=True)    # 删除第1列

df.drop(df.columns[0:3], axis=1, inplace=True)   # 删除前3列

df.drop(df.columns[[0, 2]], axis=1, inplace=True) # 删除第1第3列 

df.loc['5'] = [16, 17, 18, 19]  # 后面的序列是Iterable就行

df.at['5'] = [16, 17, 18, 19]

df.set_value('5', df.columns, [16, 17, 18, 19], takeable=False)  # warning，set_value会被取消 

s = pd.Series([16, 17, 18, 19], index=df.columns, name='5')

df = df.append(s) 

s = pd.Series([16, 17, 18, 19], index=df.columns)

df = df.append(s, ignore_index=True)　

ls = [{'A': 16, 'B': 17, 'C': 18, 'D': 19}, {'A': 20, 'B': 21, 'C': 22, 'D': 23}]

df = df.append(ls, ignore_index=True) 

df.loc[len(df)] = [16, 17, 18, 19] 

df = df.reindex(index=df.index.insert(2, '5'))

df.loc['5'] = [16, 17, 18, 19] 

s = [a + c for a, c in zip(df['A'], df['C'])]     # 通过遍历获取序列

s = [row['A'] + row['C'] for i, row in df.iterrows()] # 通过iterrows()获取序列，s为list

s = df.apply(lambda row: row['A'] + row['C'], axis=1) # 通过apply获取序列，s为Series

s = df['A'] + df['C']                 # 通过Series矢量相加获取序列

s = df['A'].values + df['C'].values          # 通过Numpy矢量相加获取序列 

df.loc[:, 'E'] = s

df['E'] = s 

df.insert(0, 'E', s)  

s = pd.Series([16, 17, 18, 19], name='E', index=df.index)
df = pd.concat([df, s], axis=1) 

df['E'] = None # 需事先创建e列，否则iloc遍历会报错，loc遍历无需事先创建

for i in range(len(df)):

  df['E'].iloc[i] = df['A'].iloc[i] + df['C'].iloc[i] 

# SettingWithCopyWarning:　A value is trying to be set on a copy of a slice from a DataFrame 

df['E'] = None

col_no = [i for i in df.columns].index('E') 

for i in range(len(df)):

  df.iloc[i, col_no] = df['A'].iloc[i] + df['C'].iloc[i] 

for i in df.index:

  df.loc[i, 'E'] = df.loc[i, 'A'] + df.loc[i, 'C'] 

df[len(df)] = [16, 17, 18, 19] 

df = pd.concat([df, pd.DataFrame(columns=list('EFG'))])  # 列的次序无法指定，并且fillna时会对整个df做出调整

df = df.reindex(columns=list('ABCDEFG'), fill_value=0)  # 列的次序按照list指定，并且fill_value只对新增列做出调整，推荐！