Htykuut

I am struggling to display all the decimals coming from a json feed when I use pandas to convert the data. The code is the following.

import pandas as pd



url = 'https://api.binance.com/api/v1/klines?interval=1m&symbol=VETBTC'

df = pd.read_json(url, orient='columns', precise_float=True)



df.columns = ["Open_time","Open","High","Low","Close","Volume","Close_time","Quote_AV","TradesNo","Taker_base","Taker_quote","Ignore"]

df['Open_time'] = pd.to_datetime(df['Open_time'],unit='ms')

df['Close_time'] = pd.to_datetime(df['Close_time'],unit='ms')



print df.head(10000)

The output is:

             Open_time      Open      High       Low     Close   Volume              Close_time  Quote_AV  TradesNo  Taker_base  Taker_quote  Ignore

0   2018-11-21 02:53:00  0.000001  0.000001  0.000001  0.000001    64166 2018-11-21 02:53:59.999  0.077268         6       44229     0.053344       0

1   2018-11-21 02:54:00  0.000001  0.000001  0.000001  0.000001     5030 2018-11-21 02:54:59.999  0.005996         2        1010     0.001212       0

2   2018-11-21 02:55:00  0.000001  0.000001  0.000001  0.000001    61463 2018-11-21 02:55:59.999  0.073756         2       61463     0.073756       0

3   2018-11-21 02:56:00  0.000001  0.000001  0.000001  0.000001   106492 2018-11-21 02:56:59.999  0.127790         2      106492     0.127790       0

4   2018-11-21 02:57:00  0.000001  0.000001  0.000001  0.000001    13215 2018-11-21 02:57:59.999  0.015858         1       13215     0.015858       0

5   2018-11-21 02:58:00  0.000001  0.000001  0.000001  0.000001    25991 2018-11-21 02:58:59.999  0.031181         2       25142     0.030170       0

6   2018-11-21 02:59:00  0.000001  0.000001  0.000001  0.000001  2024424 2018-11-21 02:59:59.999  2.429309        14     1157504     1.389005       0

7   2018-11-21 03:00:00  0.000001  0.000001  0.000001  0.000001     6500 2018-11-21 03:00:59.999  0.007865         1        6500     0.007865       0

8   2018-11-21 03:01:00  0.000001  0.000001  0.000001  0.000001    24128 2018-11-21 03:01:59.999  0.028954         2           0     0.000000       0

9   2018-11-21 03:02:00  0.000001  0.000001  0.000001  0.000001  1126289 2018-11-21 03:02:59.999  1.351547         2           0     0.000000       0

10  2018-11-21 03:03:00  0.000001  0.000001  0.000001  0.000001    91099 2018-11-21 03:03:59.999  0.109695         6       37571     0.045461       0

11  2018-11-21 03:04:00  0.000001  0.000001  0.000001  0.000001    71152 2018-11-21 03:04:59.999  0.086094         1       71152     0.086094       0

12  2018-11-21 03:05:00  0.000001  0.000001  0.000001  0.000001    12222 2018-11-21 03:05:59.999  0.014789         2       12222     0.014789       0

While the json feed has values with more decimals just like:

0 1542768840000
1 "0.00000119"
2 "0.00000120"
3 "0.00000119"
4 "0.00000120"
5 "5030.00000000"
6 1542768899999
7 "0.00599580"
8 2
9 "1010.00000000"
10 "0.00121200"
11 "0"

I tried using the precise_float option but it doesn't seem to do what it is supposed to. Any help would be highly appreciated.

import pandas as pd

import ssl

ssl._create_default_https_context = ssl._create_unverified_context



url = 'https://api.binance.com/api/v1/klines?interval=1m&symbol=VETBTC'

df = pd.read_json(url, orient='columns', precise_float=True)



df.columns = ["Open_time","Open","High","Low","Close","Volume","Close_time","Quote_AV","TradesNo","Taker_base","Taker_quote","Ignore"]

df['Open_time'] = pd.to_datetime(df['Open_time'],unit='ms')

df['Close_time'] = pd.to_datetime(df['Close_time'],unit='ms')



print(df.head())

OutPut:

            Open_time      Open      High       Low     Close  Volume  

0 2018-11-21 03:17:00  0.000001  0.000001  0.000001  0.000001  960188   

1 2018-11-21 03:18:00  0.000001  0.000001  0.000001  0.000001   89803   

2 2018-11-21 03:19:00  0.000001  0.000001  0.000001  0.000001       0   

3 2018-11-21 03:20:00  0.000001  0.000001  0.000001  0.000001       0   

4 2018-11-21 03:21:00  0.000001  0.000001  0.000001  0.000001  438661   



               Close_time  Quote_AV  TradesNo  Taker_base  Taker_quote  Ignore  

0 2018-11-21 03:17:59.999  1.152354         5       12795     0.015482       0  

1 2018-11-21 03:18:59.999  0.108186         6       42283     0.051162       0  

2 2018-11-21 03:19:59.999  0.000000         0           0     0.000000       0  

3 2018-11-21 03:20:59.999  0.000000         0           0     0.000000       0  

4 2018-11-21 03:21:59.999  0.526410         8        1714     0.002074       0  



​

Setting precision:

pd.set_option('precision', 15)

print(df.head())

Output:

            Open_time        Open        High         Low       Close  Volume  

0 2018-11-21 03:13:00  0.00000121  0.00000121  0.00000121  0.00000121    7231   

1 2018-11-21 03:14:00  0.00000121  0.00000121  0.00000121  0.00000121   22162   

2 2018-11-21 03:15:00  0.00000120  0.00000120  0.00000120  0.00000120    1000   

3 2018-11-21 03:16:00  0.00000121  0.00000121  0.00000120  0.00000120   83247   

4 2018-11-21 03:17:00  0.00000120  0.00000121  0.00000120  0.00000121  960188   



               Close_time    Quote_AV  TradesNo  Taker_base  Taker_quote  

0 2018-11-21 03:13:59.999  0.00874951         1        7231   0.00874951   

1 2018-11-21 03:14:59.999  0.02681602         3       22162   0.02681602   

2 2018-11-21 03:15:59.999  0.00120000         1           0   0.00000000   

3 2018-11-21 03:16:59.999  0.10062838         7       73198   0.08856958   

4 2018-11-21 03:17:59.999  1.15235355         5       12795   0.01548195   



   Ignore  

0       0  

1       0  

2       0  

3       0  

4       0  

Reference: https://pandas.pydata.org/pandas-docs/stable/options.html#setting-startup-options-in-python-ipython-environment

Pandas has different options to set the way a float can be displayed.
Check it here https://pandas.pydata.org/pandas-docs/stable/options.html

In your case, assuming you have 8 chars after 0. , one solution can be

import pandas as pd

pd.options.display.float_format = '{:,.8f}'.format