Pandas json_load misses decimals
1
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.
python pandas binance
asked Nov 21 '18 at 11:16
Zoltan veress
82
add a comment |
1
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.
python pandas binance
asked Nov 21 '18 at 11:16
Zoltan veress
82
Can you try settingpd.set_option('precision', 10)
– Srce Cde
Nov 21 '18 at 11:23
set_option() will help you to set precision level in the pandas. @Chirag is right. try to put precision level according to your need. you can read this paper here pandas.pydata.org/pandas-docs/stable/options.html
– Imran Ahmad Ghazali
Nov 21 '18 at 11:28
add a comment |
1
1
1
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.
python pandas binance
asked Nov 21 '18 at 11:16
Zoltan veress
82
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.
python pandas binance
python pandas binance
asked Nov 21 '18 at 11:16
Zoltan veress
82
asked Nov 21 '18 at 11:16
Zoltan veress
82
asked Nov 21 '18 at 11:16
Zoltan veress
82
asked Nov 21 '18 at 11:16
Zoltan veress
82
asked Nov 21 '18 at 11:16
Zoltan veress
82
82
Can you try settingpd.set_option('precision', 10)
– Srce Cde
Nov 21 '18 at 11:23
set_option() will help you to set precision level in the pandas. @Chirag is right. try to put precision level according to your need. you can read this paper here pandas.pydata.org/pandas-docs/stable/options.html
– Imran Ahmad Ghazali
Nov 21 '18 at 11:28
add a comment |
Can you try settingpd.set_option('precision', 10)
– Srce Cde
Nov 21 '18 at 11:23
set_option() will help you to set precision level in the pandas. @Chirag is right. try to put precision level according to your need. you can read this paper here pandas.pydata.org/pandas-docs/stable/options.html
– Imran Ahmad Ghazali
Nov 21 '18 at 11:28
Can you try setting
pd.set_option('precision', 10)– Srce Cde
Nov 21 '18 at 11:23
Can you try setting
pd.set_option('precision', 10)– Srce Cde
Nov 21 '18 at 11:23
set_option() will help you to set precision level in the pandas. @Chirag is right. try to put precision level according to your need. you can read this paper here pandas.pydata.org/pandas-docs/stable/options.html
– Imran Ahmad Ghazali
Nov 21 '18 at 11:28
set_option() will help you to set precision level in the pandas. @Chirag is right. try to put precision level according to your need. you can read this paper here pandas.pydata.org/pandas-docs/stable/options.html
– Imran Ahmad Ghazali
Nov 21 '18 at 11:28
add a comment |
2 Answers
2
active
oldest
votes
0
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
answered Nov 21 '18 at 11:36
Srce Cde
1,134511
add a comment |
0
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
answered Nov 21 '18 at 11:28
alec_djinn
2,35021835
add a comment |
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2 Answers
2
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oldest
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2 Answers
2
active
oldest
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active
oldest
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active
oldest
votes
0
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
answered Nov 21 '18 at 11:36
Srce Cde
1,134511
add a comment |
0
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
answered Nov 21 '18 at 11:36
Srce Cde
1,134511
add a comment |
0
0
0
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
answered Nov 21 '18 at 11:36
Srce Cde
1,134511
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
answered Nov 21 '18 at 11:36
Srce Cde
1,134511
edited Nov 21 '18 at 11:42
answered Nov 21 '18 at 11:36
Srce Cde
1,134511
answered Nov 21 '18 at 11:36
Srce Cde
1,134511
answered Nov 21 '18 at 11:36
Srce Cde
1,134511
1,134511
add a comment |
add a comment |
0
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
answered Nov 21 '18 at 11:28
alec_djinn
2,35021835
add a comment |
0
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
answered Nov 21 '18 at 11:28
alec_djinn
2,35021835
add a comment |
0
0
0
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
answered Nov 21 '18 at 11:28
alec_djinn
2,35021835
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
answered Nov 21 '18 at 11:28
alec_djinn
2,35021835
answered Nov 21 '18 at 11:28
alec_djinn
2,35021835
answered Nov 21 '18 at 11:28
alec_djinn
2,35021835
answered Nov 21 '18 at 11:28
alec_djinn
2,35021835
2,35021835
add a comment |
add a comment |
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Can you try setting
pd.set_option('precision', 10)– Srce Cde
Nov 21 '18 at 11:23
set_option() will help you to set precision level in the pandas. @Chirag is right. try to put precision level according to your need. you can read this paper here pandas.pydata.org/pandas-docs/stable/options.html
– Imran Ahmad Ghazali
Nov 21 '18 at 11:28