(1)将下面的字典创建为DataFrame<br>data = {"grammer":["Python","C","Java","GO",np.nan,"SQL","PHP","Python"]

下面是将字典创建为DataFrame的代码：

import pandas as pd
import numpy as np

data = {"grammer":['Python', 'C', 'Java', 'GO', np.nan, 'SQL', 'PHP', 'Python'], 
        "score":[1.0, 2.0, np.nan, 4.0, 5.0, 6.0, 7.0,10.0]}
df = pd.DataFrame(data)
print(df)

输出结果为：

  grammer  score
0  Python    1.0
1       C    2.0
2    Java    NaN
3      GO    4.0
4     NaN    5.0
5     SQL    6.0
6     PHP    7.0
7  Python   10.0

相关问题

将以下字典创建为DataFrame; data={"grammer":[python',"c","java","go",np. nan,"sq[","python"],"score":[1,2,np.

可以使用pandas库中的DataFrame函数将字典类型转换为DataFrame类型。具体代码如下所示：

import pandas as pd
import numpy as np

data = {"grammer": ["python", "c", "java", "go", np.nan, "sq[", "python"], "score": [1, 2, np.nan, 4, 5, 6, 7]}
df = pd.DataFrame(data)

其中，字典data中的键值对分别对应DataFrame中的列名和列数据。在创建DataFrame时，缺失值可以用np.nan表示。

1.将下面的字典创建为DataFrame data+=+{"grammer":["Python","C","Java","GO",np.nan,"SQL","PHP","Python"],

以下是将字典创建为DataFrame的代码示例：

import pandas as pd
import numpy as np

data = {"grammer":['python','c','java','go',np.NaN,'SQL','PHP','Python'], "score":[1.0, 2.0, np.NaN, 4.0, 5.0, 6.0, 7.0, 10.0]}
df = pd.DataFrame(data)
print(df)

输出结果为：

  grammer  score
0  python    1.0
1       c    2.0
2    java    NaN
3      go    4.0
4     NaN    5.0
5     SQL    6.0
6     PHP    7.0
7  Python   10.0