pandas.read_csv of S3 gets inconsistent results within / outside Docker
## Overview
I found that the number of lines increased when I read a csv file by `pd.read_csv` from S3.
I also confirmed that several rows are duplicated with the transformation of types (their values are transformed into object-type).

This might be related to [pandas.read_csv duplicate issue](https://stackoverflow.com/questions/51790913/pandas-read-csv-duplicate-issue) , but I cannot solve the problem by removing the `.pyc` extension file.

Do you have any ideas about the reason for this phenomenon?
And how should I fix the problem?

## Details
The number of lines of the original S3 file is 34715 (one line is a header).
```
***$ wc -l data/foo.csv 
   34715 data/foo.csv
```

I build and run the following docker image.
Dockerfile:
```
FROM python:3.6.8-stretch

WORKDIR .
RUN set -x \
    apt-get -y install postgresql
COPY ./foo/requirements.txt ./
RUN pip3 install --upgrade pip
RUN pip3 install --no-cache-dir -r requirements.txt
COPY . .
```
pipfile:
```
antiorm==1.2.1
boto3==1.9.218
botocore==1.12.218
certifi==2019.6.16
chardet==3.0.4
connection==2019.4.13
db==0.1.1
docutils==0.15.2
execute==1.2
fsspec==0.4.3
idna==2.8
Jinja2==2.10.1
jinjasql==0.1.7
jmespath==0.9.4
luigi==2.8.8
MarkupSafe==1.1.1
numpy==1.17.0
pandas==0.25.2
psycopg2==2.8.3
public==2019.4.13
python-dateutil==2.8.0
pytz==2019.1
PyYAML==5.1.1
requests==2.22.0
s3fs==0.3.3
s3transfer==0.2.1
six==1.12.0
urllib3==1.25.3
yml==0.0.1
```
docker commands:
```
docker build -t pd-test -f ./Dockerfile . 
docker run -it pd-test /bin/bash
```

The number of rows (`df.shape[0]`) increases: 34714 -> 34851
```
$ python
>>> import pandas as pd
>>> df = pd.read_csv("s3://*/.*csv", converters={"foo": str})
sys:1: DtypeWarning: Columns (2,4,6,11,14,17,19,22,25,28,31,34,37,40,46,50,54,55,57,58,59,62,67,68,69,70,74,76,77) have mixed types. Specify dtype option on import or set low_memory=False.
>>> df.shape
(34851, 78)
>>> pd.__version__
'0.25.2'
>>> s3fs.__version__
'0.3.3'
```
Column names are included in the dataframe.
```
>>> df.iloc[34714].values == df.columns
array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True])
```
I also found that `df.iloc[0]` and `df.iloc[34715]` were the same except that the type of `df.iloc[34715]` was `object`.

On the other hand, the number of lines did not increase without the docker environment.
```
In [2]: df = pd.read_csv("s3://*/*.csv", converters={"foo": str})
*/.pyenv/versions/3.6.1/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (46) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
In [3]: df.shape
Out[3]: (34714, 78)

In [9]: pd.__version__
Out[9]: '0.25.2'

In [11]: s3fs.__version__
Out[11]: '0.3.3'
```