Python 熊猫：合并两个数据帧后如何防止科学记数法？

我想合并两个数据帧，但是，其中一列包含

nan

，合并后，用科学记数法记录整型值。实际上，我只想得到它们的原始值。 输入：

import pandas as pd 
import numpy as np 
left=pd.DataFrame({'key':['one','two','three'],'other':[1,2,3]})

right=pd.DataFrame({'id':[600608457536718400,np.nan,96436390593326400],'key':['one','two','three']})

total=pd.merge(left,right,on=['key'],how='left')
print(left)
print(right)
print(total)

     key  other
0    one      1
1    two      2
2  three      3
             id    key
0  6.006085e+17    one
1           NaN    two
2  9.643639e+16  three
     key  other            id
0    one      1  6.006085e+17
1    two      2           NaN
2  three      3  9.643639e+16

     key  other            id
0    one      1  600608457536718400
1    two      2           NaN
2  three      3  96436390593326400

right['id'].fillna(-1,inplace=True)
right['id']=right['id'].astype('int64')
total=pd.merge(left,right,on=['key'],how='left')

     key  other                  id
0    one      1  600608457536718336
1    two      2                  -1
2  three      3   96436390593326400

输出：

import pandas as pd 
import numpy as np 
left=pd.DataFrame({'key':['one','two','three'],'other':[1,2,3]})

right=pd.DataFrame({'id':[600608457536718400,np.nan,96436390593326400],'key':['one','two','three']})

total=pd.merge(left,right,on=['key'],how='left')
print(left)
print(right)
print(total)

     key  other
0    one      1
1    two      2
2  three      3
             id    key
0  6.006085e+17    one
1           NaN    two
2  9.643639e+16  three
     key  other            id
0    one      1  6.006085e+17
1    two      2           NaN
2  three      3  9.643639e+16

     key  other            id
0    one      1  600608457536718400
1    two      2           NaN
2  three      3  96436390593326400

right['id'].fillna(-1,inplace=True)
right['id']=right['id'].astype('int64')
total=pd.merge(left,right,on=['key'],how='left')

     key  other                  id
0    one      1  600608457536718336
1    two      2                  -1
2  three      3   96436390593326400

期望值：

import pandas as pd 
import numpy as np 
left=pd.DataFrame({'key':['one','two','three'],'other':[1,2,3]})

right=pd.DataFrame({'id':[600608457536718400,np.nan,96436390593326400],'key':['one','two','three']})

total=pd.merge(left,right,on=['key'],how='left')
print(left)
print(right)
print(total)

     key  other
0    one      1
1    two      2
2  three      3
             id    key
0  6.006085e+17    one
1           NaN    two
2  9.643639e+16  three
     key  other            id
0    one      1  6.006085e+17
1    two      2           NaN
2  three      3  9.643639e+16

     key  other            id
0    one      1  600608457536718400
1    two      2           NaN
2  three      3  96436390593326400

right['id'].fillna(-1,inplace=True)
right['id']=right['id'].astype('int64')
total=pd.merge(left,right,on=['key'],how='left')

     key  other                  id
0    one      1  600608457536718336
1    two      2                  -1
2  three      3   96436390593326400

我尝试填充列

id

，然后转换列的类型，但失败

我的尝试：

import pandas as pd 
import numpy as np 
left=pd.DataFrame({'key':['one','two','three'],'other':[1,2,3]})

right=pd.DataFrame({'id':[600608457536718400,np.nan,96436390593326400],'key':['one','two','three']})

total=pd.merge(left,right,on=['key'],how='left')
print(left)
print(right)
print(total)

     key  other
0    one      1
1    two      2
2  three      3
             id    key
0  6.006085e+17    one
1           NaN    two
2  9.643639e+16  three
     key  other            id
0    one      1  6.006085e+17
1    two      2           NaN
2  three      3  9.643639e+16

     key  other            id
0    one      1  600608457536718400
1    two      2           NaN
2  three      3  96436390593326400

right['id'].fillna(-1,inplace=True)
right['id']=right['id'].astype('int64')
total=pd.merge(left,right,on=['key'],how='left')

     key  other                  id
0    one      1  600608457536718336
1    two      2                  -1
2  three      3   96436390593326400

输出：

import pandas as pd 
import numpy as np 
left=pd.DataFrame({'key':['one','two','three'],'other':[1,2,3]})

right=pd.DataFrame({'id':[600608457536718400,np.nan,96436390593326400],'key':['one','two','three']})

total=pd.merge(left,right,on=['key'],how='left')
print(left)
print(right)
print(total)

     key  other
0    one      1
1    two      2
2  three      3
             id    key
0  6.006085e+17    one
1           NaN    two
2  9.643639e+16  three
     key  other            id
0    one      1  6.006085e+17
1    two      2           NaN
2  three      3  9.643639e+16

     key  other            id
0    one      1  600608457536718400
1    two      2           NaN
2  three      3  96436390593326400

right['id'].fillna(-1,inplace=True)
right['id']=right['id'].astype('int64')
total=pd.merge(left,right,on=['key'],how='left')

     key  other                  id
0    one      1  600608457536718336
1    two      2                  -1
2  three      3   96436390593326400

---

希望能得到帮助！谢谢

你能试试用这个吗？也许它会起作用。基本上，在创建dataframe时，我将类型更改为str

import pandas as pd 
import numpy as np 
left=pd.DataFrame({'key':['one','two','three'],'other':[1,2,3]})

right=pd.DataFrame({'id':[str(600608457536718400),np.nan,str(96436390593326400)],'key':['one','two','three']})

total=pd.merge(left,right,on=['key'],how='left')
print(total)

这将产生以下输出

     key  other                  id
0    one      1  600608457536718400
1    two      2                 NaN
2  three      3   96436390593326400

请参阅：