Python 如何将数据分隔为两个数据帧
我有一个与json数据的链接，我想从中将数据分离为两个数据帧 我的代码如下：Python 如何将数据分隔为两个数据帧,python,json,python-3.x,pandas,dataframe,Python,Json,Python 3.x,Pandas,Dataframe,我有一个与json数据的链接，我想从中将数据分离为两个数据帧 我的代码如下： import pandas as pd import requests pd.set_option('display.max_rows', 50000) pd.set_option('display.max_columns', 100) pd.set_option('display.width', 10000) headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10
import pandas as pd
import requests
pd.set_option('display.max_rows', 50000)
pd.set_option('display.max_columns', 100)
pd.set_option('display.width', 10000)
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36'}
url = "https://www.nseindia.com/api/option-chain-indices?symbol=BANKNIFTY"
data = requests.get(url, headers=headers).json()
for x in range(len(data['records']['data'])):
print(data['records']['data'][x])
{'strikePrice': 13900, 'expiryDate': '23-Apr-2020', 'PE': {'strikePrice': 13900, 'expiryDate': '23-Apr-2020', 'underlying': 'BANKNIFTY', 'identifier': 'OPTIDXBANKNIFTY23-04-2020PE13900.00', 'openInterest': 1597, 'changeinOpenInterest': 1589, 'pchangeinOpenInterest': 19862.5, 'totalTradedVolume': 9101, 'impliedVolatility': 110.76, 'lastPrice': 1.65, 'change': -1.5, 'pChange': -47.61904761904762, 'totalBuyQuantity': 49800, 'totalSellQuantity': 17560, 'bidQty': 40, 'bidprice': 1.75, 'askQty': 460, 'askPrice': 2.35, 'underlyingValue': 20681.45}, 'CE': {'strikePrice': 13900, 'expiryDate': '23-Apr-2020', 'underlying': 'BANKNIFTY', 'identifier': 'OPTIDXBANKNIFTY23-04-2020CE13900.00', 'openInterest': 0, 'changeinOpenInterest': 0, 'pchangeinOpenInterest': 0, 'totalTradedVolume': 2, 'impliedVolatility': 162.07, 'lastPrice': 6901.1, 'change': 3502.4000000000005, 'pChange': 103.05116662253218, 'totalBuyQuantity': 2620, 'totalSellQuantity': 2620, 'bidQty': 200, 'bidprice': 6629.85, 'askQty': 200, 'askPrice': 7208.75, 'underlyingValue': 20681.45}}
{'strikePrice': 13900, 'expiryDate': '30-Apr-2020', 'PE': {'strikePrice': 13900, 'expiryDate': '30-Apr-2020', 'underlying': 'BANKNIFTY', 'identifier': 'OPTIDXBANKNIFTY30-04-2020PE13900.00', 'openInterest': 989, 'changeinOpenInterest': 12, 'pchangeinOpenInterest': 1.2282497441146367, 'totalTradedVolume': 134, 'impliedVolatility': 98.26, 'lastPrice': 16.3, 'change': -4.899999999999999, 'pChange': -23.113207547169807, 'totalBuyQuantity': 32900, 'totalSellQuantity': 4100, 'bidQty': 20, 'bidprice': 16.3, 'askQty': 20, 'askPrice': 17, 'underlyingValue': 20681.45}, 'CE': {'strikePrice': 13900, 'expiryDate': '30-Apr-2020', 'underlying': 'BANKNIFTY', 'identifier': 'OPTIDXBANKNIFTY30-04-2020CE13900.00', 'openInterest': 1, 'changeinOpenInterest': 0, 'pchangeinOpenInterest': 0, 'totalTradedVolume': 0, 'impliedVolatility': 0, 'lastPrice': 5000, 'change': -5000, 'pChange': -100, 'totalBuyQuantity': 2640, 'totalSellQuantity': 2840, 'bidQty': 20, 'bidprice': 6242.05, 'askQty': 20, 'askPrice': 7401.65, 'underlyingValue': 20681.45}}
{'strikePrice': 13900, 'expiryDate': '14-May-2020', 'PE': {'strikePrice': 13900, 'expiryDate': '14-May-2020', 'underlying': 'BANKNIFTY', 'identifier': 'OPTIDXBANKNIFTY14-05-2020PE13900.00', 'openInterest': 0, 'changeinOpenInterest': 0, 'pchangeinOpenInterest': 0, 'totalTradedVolume': 0, 'impliedVolatility': 0, 'lastPrice': 0, 'change': 0, 'pChange': -100, 'totalBuyQuantity': 100, 'totalSellQuantity': 0, 'bidQty': 100, 'bidprice': 0.3, 'askQty': 0, 'askPrice': 0, 'underlyingValue': 20681.45}, 'CE': {'strikePrice': 13900, 'expiryDate': '14-May-2020', 'underlying': 'BANKNIFTY', 'identifier': 'OPTIDXBANKNIFTY14-05-2020CE13900.00', 'openInterest': 0, 'changeinOpenInterest': 0, 'pchangeinOpenInterest': 0, 'totalTradedVolume': 0, 'impliedVolatility': 0, 'lastPrice': 0, 'change': 0, 'pChange': -100, 'totalBuyQuantity': 2420, 'totalSellQuantity': 2420, 'bidQty': 2420, 'bidprice': 6223.45, 'askQty': 2420, 'askPrice': 7565.05, 'underlyingValue': 20681.45}}
['strikePrice','expiryDate', 'underlying', 'identifier', 'openInterest', 'changeinOpenInterest', 'pchangeinOpenInterest', 'totalTradedVolume','impliedVolatility', 'lastPrice', 'change', 'pChange', 'totalBuyQuantity', 'totalSellQuantity', 'bidQty', 'bidprice', 'askQty', 'askPrice', 'underlyingValue']
下面是一个示例，它展示了实现您所寻找目标的一种方法：
# initialize dataframes to hold data
pe_df = pd.DataFrame()
ce_df = pd.DataFrame()
# loop through records
for x in range(len(data['records']['data'])):
# extract the record
record = data['records']['data'][x]
# print the record
print(record)
# extract the 'PE' and 'CE' data
pe_row = record['PE']
ce_row = record['CE']
# append the row to the appropriate dataframe
pe_df = pe_df.append(pd.Series(pe_row), ignore_index=True)
ce_df = ce_df.append(pd.Series(ce_row), ignore_index=True)
我假设记录数据的格式与您在问题中显示的格式相同。以下是一个示例，展示了实现您所寻找的目标的一种方法：
# initialize dataframes to hold data
pe_df = pd.DataFrame()
ce_df = pd.DataFrame()
# loop through records
for x in range(len(data['records']['data'])):
# extract the record
record = data['records']['data'][x]
# print the record
print(record)
# extract the 'PE' and 'CE' data
pe_row = record['PE']
ce_row = record['CE']
# append the row to the appropriate dataframe
pe_df = pe_df.append(pd.Series(pe_row), ignore_index=True)
ce_df = ce_df.append(pd.Series(ce_row), ignore_index=True)
我假设记录数据是您在问题中显示的格式。假设
json_objpe = pd.DataFrame.from_dict(json_obj['PE'], orient='index').reset_index()
pe.columns = ['param', 'value'] # Optional renaming of columns
print(pe)
"""
param value
0 strikePrice 13900
1 expiryDate 23-Apr-2020
2 underlying BANKNIFTY
3 identifier OPTIDXBANKNIFTY23-04-2020PE13900.00
4 openInterest 1597
5 changeinOpenInterest 1589
6 pchangeinOpenInterest 19862.5
7 totalTradedVolume 9101
8 impliedVolatility 110.76
9 lastPrice 1.65
10 change -1.5
11 pChange -47.619
12 totalBuyQuantity 49800
13 totalSellQuantity 17560
14 bidQty 40
15 bidprice 1.75
16 askQty 460
17 askPrice 2.35
18 underlyingValue 20681.5
"""
类似地，重复上述步骤将
CEjson_objpe = pd.DataFrame.from_dict(json_obj['PE'], orient='index').reset_index()
pe.columns = ['param', 'value'] # Optional renaming of columns
print(pe)
"""
param value
0 strikePrice 13900
1 expiryDate 23-Apr-2020
2 underlying BANKNIFTY
3 identifier OPTIDXBANKNIFTY23-04-2020PE13900.00
4 openInterest 1597
5 changeinOpenInterest 1589
6 pchangeinOpenInterest 19862.5
7 totalTradedVolume 9101
8 impliedVolatility 110.76
9 lastPrice 1.65
10 change -1.5
11 pChange -47.619
12 totalBuyQuantity 49800
13 totalSellQuantity 17560
14 bidQty 40
15 bidprice 1.75
16 askQty 460
17 askPrice 2.35
18 underlyingValue 20681.5
"""
同样，重复上述步骤将
CEimport pandas as pd
import requests
pd.set_option('display.max_rows', 50000)
pd.set_option('display.max_columns', 100)
pd.set_option('display.width', 10000)
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36'}
url = "https://www.nseindia.com/api/option-chain-indices?symbol=BANKNIFTY"
records = requests.get(url, headers=headers).json()
df_PE = pd.DataFrame()
df_CE = pd.DataFrame()
# print(len(records['records']['data']))
for x in range(len(records['records']['data'])):
if records['records']['data'][x]['expiryDate'] == "23-Apr-2020":
try:
PE = pd.DataFrame([records['records']['data'][x]['PE']])
df_PE = df_PE.append(PE)
except Exception as err:
pass
try:
CE = pd.DataFrame([records['records']['data'][x]['CE']])
df_CE = df_CE.append(CE)
except Exception as err:
pass
print(df_PE)
print(df_CE)
我已解决了以下问题。有没有其他有效的方法来达到预期的效果
import pandas as pd
import requests
pd.set_option('display.max_rows', 50000)
pd.set_option('display.max_columns', 100)
pd.set_option('display.width', 10000)
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36'}
url = "https://www.nseindia.com/api/option-chain-indices?symbol=BANKNIFTY"
records = requests.get(url, headers=headers).json()
df_PE = pd.DataFrame()
df_CE = pd.DataFrame()
# print(len(records['records']['data']))
for x in range(len(records['records']['data'])):
if records['records']['data'][x]['expiryDate'] == "23-Apr-2020":
try:
PE = pd.DataFrame([records['records']['data'][x]['PE']])
df_PE = df_PE.append(PE)
except Exception as err:
pass
try:
CE = pd.DataFrame([records['records']['data'][x]['CE']])
df_CE = df_CE.append(CE)
except Exception as err:
pass
print(df_PE)
print(df_CE)
headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36'
}
url = "https://www.nseindia.com/api/option-chain-indices?symbol=BANKNIFTY"
data = requests.get(url, headers=headers).json()
#list comprehension here
#note that it is nested, since the data we are truly keen on
#is embedded in a list
#this will give us a tuple of the key, and the dataframe of the values
#as the values are dictionaries
res = [[(key, pd.DataFrame.from_dict(value, orient='index').T)
for key, value in entry.items()
if key in ['PE', 'CE']]
for entry in data['records']['data']]
from collections import defaultdict
from itertools import chain
d = defaultdict(list)
#group the values into a pair
#they will be a combined list of dataframes
#belonging to either PE or CE
for k, v in chain.from_iterable(res):
d[k].append(v)
#now we can merge the values
#and keep our result as a dictionary
#this allows us to access PE or CE via keys
result = {key: pd.concat(values) for key, values in d.items()}
#now, we can access either PE or CE
#dataframe is quite long, so this is a small part of it
result['PE'].iloc[:3,:5]
strikePrice expiryDate underlying identifier openInterest
0 13900 23-Apr-2020 BANKNIFTY OPTIDXBANKNIFTY23-04-2020PE13900.00 1597
0 13900 30-Apr-2020 BANKNIFTY OPTIDXBANKNIFTY30-04-2020PE13900.00 989
0 13900 14-May-2020 BANKNIFTY OPTIDXBANKNIFTY14-05-2020PE13900.00 0
headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36'
}
url = "https://www.nseindia.com/api/option-chain-indices?symbol=BANKNIFTY"
data = requests.get(url, headers=headers).json()
#list comprehension here
#note that it is nested, since the data we are truly keen on
#is embedded in a list
#this will give us a tuple of the key, and the dataframe of the values
#as the values are dictionaries
res = [[(key, pd.DataFrame.from_dict(value, orient='index').T)
for key, value in entry.items()
if key in ['PE', 'CE']]
for entry in data['records']['data']]
from collections import defaultdict
from itertools import chain
d = defaultdict(list)
#group the values into a pair
#they will be a combined list of dataframes
#belonging to either PE or CE
for k, v in chain.from_iterable(res):
d[k].append(v)
#now we can merge the values
#and keep our result as a dictionary
#this allows us to access PE or CE via keys
result = {key: pd.concat(values) for key, values in d.items()}
#now, we can access either PE or CE
#dataframe is quite long, so this is a small part of it
result['PE'].iloc[:3,:5]
strikePrice expiryDate underlying identifier openInterest
0 13900 23-Apr-2020 BANKNIFTY OPTIDXBANKNIFTY23-04-2020PE13900.00 1597
0 13900 30-Apr-2020 BANKNIFTY OPTIDXBANKNIFTY30-04-2020PE13900.00 989
0 13900 14-May-2020 BANKNIFTY OPTIDXBANKNIFTY14-05-2020PE13900.00 0
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