如何在 Pandas Dataframe 中增量添加行？

Question

I am calculating the open-high-low-close (OHLC) of data for a duration of each 15 mins from 9:15 to 15:30 and want to store the OHLC values in a dataframe in each new row.

ohlc = pd.DataFrame(columns=('Open','High','Low','Close'))
for row in ohlc:
    ohlc.loc[10] = pd.DataFrame([[candle_open_price,candle_high_price,candle_low_price,candle_close_price]])

But I am not able to do say getting an error of:

ValueError: cannot set a row with mismatched columns

Just I want to incrementally store the OHLC data of each 15-minute duration which I have calculated & put in rows of the new ohlc dataframe

---

EDIT

import numpy as np
import pandas as pd
import datetime as dt
import matplotlib as plt
import dateutil.parser

tradedata = pd.read_csv('ICICIBANK_TradeData.csv', index_col=False, 
              names=['Datetime','Price'], 
            header=0)
tradedata['Datetime'] =  pd.to_datetime(tradedata['Datetime'])

first_trd_time = tradedata['Datetime'][0]
last_time = dateutil.parser.parse('2016-01-01 15:30:00.000000')

candle_time = 15;
candle_number = 0

while(first_trd_time < last_time):
    candledata = tradedata[(tradedata['Datetime']>first_trd_time) & (tradedata['Datetime']<first_trd_time+dt.timedelta(minutes=candle_time))]
first_trd_time = first_trd_time+dt.timedelta(minutes=candle_time)

candle_open_price = candledata.iloc[0]['Price']
candle_open_time = candledata.iloc[0]['Datetime']
candle_close_price = candledata.iloc[-1]['Price']
candle_close_time = candledata.iloc[-1]['Datetime']
candle_high_price = candledata.loc[candledata['Price'].idxmax()]['Price']
candle_high_time = candledata.loc[candledata['Price'].idxmax()]['Datetime'] 
candle_low_price = candledata.loc[candledata['Price'].idxmin()]['Price']
candle_low_time = candledata.loc[candledata['Price'].idxmin()]['Datetime']

ohlc = pd.DataFrame(columns=('Open','High','Low','Close'))
ohlc_data = pd.DataFrame()

if(candle_number == 0):
    ohlc = pd.DataFrame(np.array([[0, 0, 0, 0]]), columns=['Open', 'High', 'Low', 'Close']).append(ohlc, ignore_index=True)
    candle_number = candle_number + 1
    print "Zeroth Candle"
else:
    ohlc.ix[candle_number] = (candle_open_price,candle_open_price,candle_open_price,candle_open_price)
    print "else part with incermenting candle_number"
    candle_number = candle_number + 1

print "first_trd_time" 
print first_trd_time
print candle_number

print "Success!"

This is my code error is

ValueError: cannot set by positional indexing with enlargement

Answer 1

IIUC you can append DataFrames for each row to list of DataFrames dfs and then concat them to df1 :

ohlc = pd.DataFrame(columns=('Open','High','Low','Close'))

dfs = []
for row in ohlc.iterrows():
    df = pd.DataFrame([candle_open_price,candle_high_price,
                        candle_low_price,candle_close_price]).T
    dfs.append(df)

df1 = pd.concat(dfs, ignore_index=True)
print (df1)

Then concat to original DataFrame ohlc :

df2 = pd.concat([ohlc,df1])
print (df2)

---

Sample (for testing in each iteration of loop are added same data):

#sample data
candle_open_price = pd.Series([1.5,10], 
                              name='Open', 
                              index=pd.DatetimeIndex(['2016-01-02','2016-01-03']) )
candle_high_price =  pd.Series([8,9], 
                               name='High', 
                               index=pd.DatetimeIndex(['2016-01-02','2016-01-03']))
candle_low_price =  pd.Series([0,12], 
                              name='Low', 
                              index=pd.DatetimeIndex(['2016-01-02','2016-01-03']))
candle_close_price =  pd.Series([4,5], 
                                name='Close', 
                                index=pd.DatetimeIndex(['2016-01-02','2016-01-03']))

data = np.array([[1,2,3,5],[7,7,8,9],[10,8,9,3]])
idx = pd.DatetimeIndex(['2016-01-08','2016-01-09','2016-01-10'])
ohlc = pd.DataFrame(data=data, 
                    columns=('Open','High','Low','Close'),
                    index=idx)
print (ohlc)
            Open  High  Low  Close
2016-01-08     1     2    3      5
2016-01-09     7     7    8      9
2016-01-10    10     8    9      3

dfs = []
for row in ohlc.iterrows():
    df = pd.DataFrame([candle_open_price,candle_high_price,
                       candle_low_price,candle_close_price]).T
    #print (df)
    dfs.append(df)

df1 = pd.concat(dfs)
print (df1)
            Open  High   Low  Close
2016-01-02   1.5   8.0   0.0    4.0
2016-01-03  10.0   9.0  12.0    5.0
2016-01-02   1.5   8.0   0.0    4.0
2016-01-03  10.0   9.0  12.0    5.0
2016-01-02   1.5   8.0   0.0    4.0
2016-01-03  10.0   9.0  12.0    5.0

df2 = pd.concat([ohlc,df1])
print (df2)
            Open  High   Low  Close
2016-01-08   1.0   2.0   3.0    5.0
2016-01-09   7.0   7.0   8.0    9.0
2016-01-10  10.0   8.0   9.0    3.0
2016-01-02   1.5   8.0   0.0    4.0
2016-01-03  10.0   9.0  12.0    5.0
2016-01-02   1.5   8.0   0.0    4.0
2016-01-03  10.0   9.0  12.0    5.0
2016-01-02   1.5   8.0   0.0    4.0
2016-01-03  10.0   9.0  12.0    5.0