

In [1]:

    
import MySQLdb as mdb
import pandas as pd
import plotly
con = mdb.connect('127.0.0.1', 'root', 'paytm@197', 'stocks');
with con:
    cur = con.cursor()
    cur.execute("select * from stock_data where symbol = 'pnb'")
    rows = cur.fetchall()
    df = pd.DataFrame( [[ij for ij in i] for i in rows] )
    print(df.head(20))









    



         0    1   2           3       4       5       6       7       8   \
0   4018073  PNB  EQ  2002-04-28   37.40   38.50   39.10   37.65   39.10   
1   4018075  PNB  EQ  2002-04-25   31.00   40.00   40.00   35.00   37.30   
2   4018076  PNB  EQ  2002-04-29   38.60   39.00   40.35   38.50   40.15   
3   4018077  PNB  EQ  2002-05-05   46.45   47.00   48.25   45.95   46.80   
4   4018078  PNB  EQ  2002-05-01   40.10   40.25   46.10   40.25   45.10   
5   4018079  PNB  EQ  2002-05-02   44.80   45.80   47.45   44.55   46.95   
6   4018080  PNB  EQ  2002-05-08   43.40   44.70   44.70   42.85   43.80   
7   4018081  PNB  EQ  2002-05-06   46.70   46.85   46.85   44.65   45.00   
8   4018083  PNB  EQ  2002-05-09   43.70   48.00   48.00   42.90   43.60   
9   4018084  PNB  EQ  2007-01-01  507.00  512.00  512.80  503.50  511.80   
10  4018085  PNB  EQ  2002-05-07   45.30   45.50   45.70   42.60   44.25   
11  4018087  PNB  EQ  2007-01-02  511.60  508.30  521.00  508.30  516.95   
12  4018088  PNB  EQ  2007-01-03  515.60  519.80  523.45  508.00  509.00   
13  4018089  PNB  EQ  2007-01-04  510.85  509.90  514.80  504.25  512.40   
14  4018090  PNB  EQ  2007-01-07  511.55  511.55  514.40  502.10  511.25   
15  4018091  PNB  EQ  2007-01-08  510.80  510.00  513.90  493.35  496.00   
16  4018092  PNB  EQ  2007-01-09  496.15  510.00  510.00  484.05  485.00   
17  4018093  PNB  EQ  2007-01-10  488.25  485.00  496.00  480.00  490.00   
18  4018094  PNB  EQ  2007-01-11  485.70  494.85  518.95  492.00  516.00   
19  4018095  PNB  EQ  2007-01-14  516.00  525.00  533.00  514.50  518.30   

        9       10       11      12  13       14  15                  16  \
0    38.60   38.26  2602886   996.0   0  1632801  63 2016-07-25 18:56:13   
1    37.40   37.03  4000760  1482.0   0  2048637  51 2016-07-25 18:56:13   
2    40.10   39.79  1377334   548.0   0        0   0 2016-07-25 18:56:13   
3    46.70   46.96  1609113   756.0   0  1041920  65 2016-07-25 18:56:13   
4    44.80   44.15  3348126  1478.0   0  1307465  39 2016-07-25 18:56:13   
5    46.45   46.10  2689672  1240.0   0  1032559  38 2016-07-25 18:56:13   
6    43.70   43.62   507464   221.0   0   317226  63 2016-07-25 18:56:13   
7    45.30   45.69   964485   441.0   0   649068  67 2016-07-25 18:56:13   
8    43.35   43.32   315013   136.0   0   214039  68 2016-07-25 18:56:13   
9   511.60  508.99   181484   924.0   0    29067  16 2016-07-25 18:56:13   
10   43.40   43.90   642380   282.0   0   399537  62 2016-07-25 18:56:13   
11  515.60  516.73   320559  1656.0   0   122611  38 2016-07-25 18:56:13   
12  510.85  517.80   410828  2127.0   0   108085  26 2016-07-25 18:56:13   
13  511.55  510.98   244874  1251.0   0    64280  26 2016-07-25 18:56:13   
14  510.80  508.64   487833  2481.0   0   221328  45 2016-07-25 18:56:13   
15  496.15  503.16   648162  3261.0   0   292064  45 2016-07-25 18:56:13   
16  488.25  488.59   556877  2721.0   0   280064  50 2016-07-25 18:56:13   
17  485.70  484.29   827043  4005.0   0   421730  51 2016-07-25 18:56:13   
18  516.00  511.94   761443  3898.0   0   307028  40 2016-07-25 18:56:13   
19  519.25  519.48   337196  1752.0   0    89907  27 2016-07-25 18:56:13   

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