pandas常用操作

DataFrame

增

创建
可以利用

• 列表(list)
• 字典(dict)
• 系列(series)
• Numpy ndarrays
• 其他数据帧(DataFrame)

来创建DataFrame对象

如：

a = pd.DataFrame(np.random.normal(0, 1, (3, 3)))
1

增加列

a['new'] = ['鹅', '鹅', '鹅']
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增加行

a.loc[3]=[0, 0, 0, '鹅']
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删

目标DataFrame

b = a.copy()
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万能删除function

df.drop()
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添加 inplace=True 则在原数据修改，默认为False

删列

①

b.drop(columns='new')
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②

c = b.pop('new')
b
c
1
2
3

赋值并在原数据中删除

③

del b['new']
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删行

b.drop(index=3)
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改查

这里改查放在一起，因为查到即可更改

目标DataFrame

a = np.random.normal(0, 1, (3, 3))
a = pd.DataFrame(np.random.normal(0, 1, (3, 3)))
a['abc'] = ['a', 'b', 'c']
1
2
3

loc

pandas loc

Access a group of rows and columns by label(s) or a boolean array.

a = np.random.normal(0, 1, (3, 3))
a = pd.DataFrame(np.random.normal(0, 1, (3, 3)))
a['abc'] = ['a', 'b', 'c']
1
2
3

a.loc[0, 0]
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0.41059850193837233

a.loc[0, 'abc']
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‘a’

a.loc[a.abc != 'b', 'abc']
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iloc

pandas iloc

Purely integer-location based indexing for selection by position.

a.iloc[0, 3]
1

a.iloc[0]
1

a.iloc[[0, 1]]
1

a.iloc[0:3]
1

a.iloc[:, 3]
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[ ]

使用[ ]进行查找

a = np.random.normal(0, 1, (3, 3))
a = pd.DataFrame(np.random.normal(0, 1, (3, 3)))
a['abc'] = ['a', 'b', 'c']
1
2
3

查单列

a[1]
1

查多列

a[[1, 'abc']]
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按单个条件查询

a[a[1] > 0.1]
1

a[a.index != 1]
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按多个条件查询

# Satisfying multiple conditions simultaneously
a[(a[1] > 0.1) & (a[0] > 0)]
1
2

# At least one condition is met
a[(a[1] > 0.1) | (a[0] > 0)]
1
2

索引和字段操作

重置

reset_index

Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.

a
1

a.index = ['c', 'd', 'e']
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a.reset_index(drop=True, inplace=True)
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重命名

rename

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error.

a.rename(index={0:9, 1:8}, inplace=True)
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a.rename(columns={0:'cba', 1:'nba'}, inplace=True)
1