I have a dataset in csv format that automatically downloads from a webservice. The csv file has the following general format:

```
csv file in excel
[Timestamp]  [Column B]
1.51258E+12  A
1.51242E+12  B
1.51242E+12  C

```

When the `['Timestamp']` formatting is changed in excel from 'General' to 'Number', the full number shows as follows: 

```
csv file (formatting changed in excel)
[Timestamp]   [Column B]
1512584017891  A
1512423886571  B
1512423818970  C

```

I need to automate the processing of the csv file, and so I cannot go into the file in excel every time to switch the format from general to timestamp. What I'm finding is that `pd.read_csv()` is importing the `['Timestamp']` csv column as scientific notation, leaving a truncated `df['Timestamp'] dtype=float64`. 

```
df (in pandas)
[Timestamp]  [Column B]
1.512580e+12  A
1.512420e+12  B
1.512420e+12  C

```

Notice how now `df['Timestamp']` added a 0 prior to the E+12 when importing. I tried to convert `df['Timestamp'].astype('int64')`, but this showed what I was worried about: pd.read\_csv() substituted the hidden digits for zeros.

```
In[1]: df['Timestamp'].astype('int64').head(3)

Out[1]: 1512580000000
        1512420000000
        1512420000000
        Name: Timestamp, dtype: int64

```

Is there a way to 1) import the right timestamp, and then 2) convert that timestamp to the following format: 12/14/2017 10:32:12 AM?