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ib_insync saving fundamentalRatios in Pandas dataframe - python

I am trying to convert or save fundamentalRatios into pandas Dataframe for further ANalysis
My code snippet is here
import pandas as pd
from ib_insync import *
from stockstats import StockDataFrame
util.startLoop()
ib = IB()
ib.connect('127.0.0.1',7497, clientId=77)
stk= Stock('TSLA', 'SMART', 'USD')
ticker = ib.reqMktData(stk, '258')
ib.sleep(5)
ticker.fundamentalRatios
OUTPUT
FundamentalRatios(TTMNPMGN=1.86323, NLOW=57.84, ACFSHR=1.558192, ALTCL=nan, TTMPRCFPS=146.5743,
TTMCFSHR=2.81231, ASFCF=-554, AEPSNORM=-0.83006, TTMRECTURN=19.53421, AATCA=12103, QCSHPS=9.26344,
TTMFCF=799.4, LATESTADATE='2020-06-30', APTMGNPCT=-2.70567, TTMNIAC=368, EV_Cur=403016.1,
QATCA=15336,
PR2TANBK=42.44729, TTMFCFSHR=0.83055, NPRICE=425.79, ASICF=1, REVTRENDGR=50.3571, QSCEX=-566,
PRICE2BK=40.1811, ALSTD=nan, AOTLO=2405, TTMPAYRAT=0, QPR2REV=65.60383, TTMREVCHG=3.0735,
TTMROAPCT=1.36842, QTOTCE=9855, APENORM=-492.4842, QLTCL=12270, QSFCF=-45, TTMROIPCT=2.11798,
DIVGRPCT=nan, QOTLO=964, TTMEPSCHG=110.1973, YIELD=0, TTMREVPS=26.70961, TTMEBT=570, ADIV5YAVG=nan,
Frac52Wk=0.8335162, NHIG=502.49, ASCEX=-1437, QTA=38135, TTMGROSMGN=19.77206, QTL=28280, AFPRD=798,
QCURRATIO=1.24988, TTMREV=25708, TTMINVTURN=5.57405, QCASH=8615, QLSTD=0, TTMOPMGN=4.7845,
TTMPR2REV=3.080634, QSICF=0, TTMNIPEREM=9975.842, EPSCHNGYR=121.7348, TTMPRFCFPS=496.3157,
TTMPTMGN=2.21721, AREVPS=27.77175, AEBTNORM=-516, ASOPI=-69, NetDebt_I=5524, PRYTDPCTR=376.09721,
TTMEBITD=3445.851, AFEEPSNTM=2.984, EPSTRENDGR=nan, QTOTD2EQ=143.4703, QSOPI=327, QBVPS=10.59677,
YLD5YAVG=nan, PR13WKPCT=40.70401, PR52WKPCT=610.4078, AROAPCT=-2.420037, QTOTLTD=9291,
TTMEPSXCLX=0.38866, QPRCFPS=445.9840754264795, QTANBVPS=10.05054, AROIPCT=-3.826488, QEBIT=327,
QEBITDA=894, MKTCAP=396754.8, TTMINTCOV=2.11532, TTMROEPCT=4.72692, TTMREVPERE=535404.9,
AEPSXCLXOR=-0.9740113, QFPRD=111, REVCHNGYR=-4.94003, AFPSS=1285, CURRENCY='USD',
EV2EBITDA_Cur=450.801, PEEXCLXOR=1095.533, QQUICKRATI=0.92241, ASINN=nan, QFPSS=57, BETA=1.91633,
ANIACNORM=-765.1497, PR1WKPCT=-5.143913, QLTD2EQ=106.139, QSINN=nan, PR4WKPCT=9.799118, AEBIT=-72)
I want to save output i.e Fundamental Ratios in pandas Dataframe
Thanks

Related

The data frame I am using: https://www.kaggle.com/mustiztemiz/diabetes
I have the following column:
Outcome - which has values 0 or 1.
I want to plot a barplot which has Outcome on the x-axis and the it's count on y-axis.
My code is as follows:
sns.barplot(x='Outcome', y=diabetes['Outcome'].value_counts(), data=diabetes)
It is returning the following plot
The output I got is wrong as 1 should be 268 in count and 0 should be 500 in count.
I don't know where I did the mistake.
diabetes.csv
Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
6,148,72,35,0,33.6,0.627,50,1
1,85,66,29,0,26.6,0.351,31,0
8,183,64,0,0,23.3,0.672,32,1
1,89,66,23,94,28.1,0.167,21,0
0,137,40,35,168,43.1,2.288,33,1
5,116,74,0,0,25.6,0.201,30,0
3,78,50,32,88,31.0,0.248,26,1
10,115,0,0,0,35.3,0.134,29,0
2,197,70,45,543,30.5,0.158,53,1
8,125,96,0,0,0.0,0.232,54,1
4,110,92,0,0,37.6,0.191,30,0
10,168,74,0,0,38.0,0.537,34,1
10,139,80,0,0,27.1,1.441,57,0
1,189,60,23,846,30.1,0.398,59,1
5,166,72,19,175,25.8,0.587,51,1
7,100,0,0,0,30.0,0.484,32,1
0,118,84,47,230,45.8,0.551,31,1
7,107,74,0,0,29.6,0.254,31,1
1,103,30,38,83,43.3,0.183,33,0
1,115,70,30,96,34.6,0.529,32,1
3,126,88,41,235,39.3,0.704,27,0
8,99,84,0,0,35.4,0.388,50,0
7,196,90,0,0,39.8,0.451,41,1
9,119,80,35,0,29.0,0.263,29,1
11,143,94,33,146,36.6,0.254,51,1
10,125,70,26,115,31.1,0.205,41,1
7,147,76,0,0,39.4,0.257,43,1
1,97,66,15,140,23.2,0.487,22,0
13,145,82,19,110,22.2,0.245,57,0
5,117,92,0,0,34.1,0.337,38,0
5,109,75,26,0,36.0,0.546,60,0
3,158,76,36,245,31.6,0.851,28,1
3,88,58,11,54,24.8,0.267,22,0
6,92,92,0,0,19.9,0.188,28,0
10,122,78,31,0,27.6,0.512,45,0
4,103,60,33,192,24.0,0.966,33,0
11,138,76,0,0,33.2,0.42,35,0
9,102,76,37,0,32.9,0.665,46,1
2,90,68,42,0,38.2,0.503,27,1
4,111,72,47,207,37.1,1.39,56,1
3,180,64,25,70,34.0,0.271,26,0
7,133,84,0,0,40.2,0.696,37,0
7,106,92,18,0,22.7,0.235,48,0
9,171,110,24,240,45.4,0.721,54,1
7,159,64,0,0,27.4,0.294,40,0
0,180,66,39,0,42.0,1.893,25,1
1,146,56,0,0,29.7,0.564,29,0
2,71,70,27,0,28.0,0.586,22,0
7,103,66,32,0,39.1,0.344,31,1
7,105,0,0,0,0.0,0.305,24,0
1,103,80,11,82,19.4,0.491,22,0
1,101,50,15,36,24.2,0.526,26,0
5,88,66,21,23,24.4,0.342,30,0
8,176,90,34,300,33.7,0.467,58,1
7,150,66,42,342,34.7,0.718,42,0
1,73,50,10,0,23.0,0.248,21,0
7,187,68,39,304,37.7,0.254,41,1
0,100,88,60,110,46.8,0.962,31,0
0,146,82,0,0,40.5,1.781,44,0
0,105,64,41,142,41.5,0.173,22,0
2,84,0,0,0,0.0,0.304,21,0
8,133,72,0,0,32.9,0.27,39,1
5,44,62,0,0,25.0,0.587,36,0
2,141,58,34,128,25.4,0.699,24,0
7,114,66,0,0,32.8,0.258,42,1
5,99,74,27,0,29.0,0.203,32,0
0,109,88,30,0,32.5,0.855,38,1
2,109,92,0,0,42.7,0.845,54,0
1,95,66,13,38,19.6,0.334,25,0
4,146,85,27,100,28.9,0.189,27,0
2,100,66,20,90,32.9,0.867,28,1
5,139,64,35,140,28.6,0.411,26,0
13,126,90,0,0,43.4,0.583,42,1
4,129,86,20,270,35.1,0.231,23,0
1,79,75,30,0,32.0,0.396,22,0
1,0,48,20,0,24.7,0.14,22,0
7,62,78,0,0,32.6,0.391,41,0
5,95,72,33,0,37.7,0.37,27,0
0,131,0,0,0,43.2,0.27,26,1
2,112,66,22,0,25.0,0.307,24,0
3,113,44,13,0,22.4,0.14,22,0
2,74,0,0,0,0.0,0.102,22,0
7,83,78,26,71,29.3,0.767,36,0
0,101,65,28,0,24.6,0.237,22,0
5,137,108,0,0,48.8,0.227,37,1
2,110,74,29,125,32.4,0.698,27,0
13,106,72,54,0,36.6,0.178,45,0
2,100,68,25,71,38.5,0.324,26,0
15,136,70,32,110,37.1,0.153,43,1
1,107,68,19,0,26.5,0.165,24,0
1,80,55,0,0,19.1,0.258,21,0
4,123,80,15,176,32.0,0.443,34,0
7,81,78,40,48,46.7,0.261,42,0
4,134,72,0,0,23.8,0.277,60,1
2,142,82,18,64,24.7,0.761,21,0
6,144,72,27,228,33.9,0.255,40,0
2,92,62,28,0,31.6,0.13,24,0
1,71,48,18,76,20.4,0.323,22,0
6,93,50,30,64,28.7,0.356,23,0
1,122,90,51,220,49.7,0.325,31,1
1,163,72,0,0,39.0,1.222,33,1
1,151,60,0,0,26.1,0.179,22,0
0,125,96,0,0,22.5,0.262,21,0
1,81,72,18,40,26.6,0.283,24,0
2,85,65,0,0,39.6,0.93,27,0
1,126,56,29,152,28.7,0.801,21,0
1,96,122,0,0,22.4,0.207,27,0
4,144,58,28,140,29.5,0.287,37,0
3,83,58,31,18,34.3,0.336,25,0
0,95,85,25,36,37.4,0.247,24,1
3,171,72,33,135,33.3,0.199,24,1
8,155,62,26,495,34.0,0.543,46,1
1,89,76,34,37,31.2,0.192,23,0
4,76,62,0,0,34.0,0.391,25,0
7,160,54,32,175,30.5,0.588,39,1
4,146,92,0,0,31.2,0.539,61,1
5,124,74,0,0,34.0,0.22,38,1
5,78,48,0,0,33.7,0.654,25,0
4,97,60,23,0,28.2,0.443,22,0
4,99,76,15,51,23.2,0.223,21,0
0,162,76,56,100,53.2,0.759,25,1
6,111,64,39,0,34.2,0.26,24,0
2,107,74,30,100,33.6,0.404,23,0
5,132,80,0,0,26.8,0.186,69,0
0,113,76,0,0,33.3,0.278,23,1
1,88,30,42,99,55.0,0.496,26,1
3,120,70,30,135,42.9,0.452,30,0
1,118,58,36,94,33.3,0.261,23,0
1,117,88,24,145,34.5,0.403,40,1
0,105,84,0,0,27.9,0.741,62,1
4,173,70,14,168,29.7,0.361,33,1
9,122,56,0,0,33.3,1.114,33,1
3,170,64,37,225,34.5,0.356,30,1
8,84,74,31,0,38.3,0.457,39,0
2,96,68,13,49,21.1,0.647,26,0
2,125,60,20,140,33.8,0.088,31,0
0,100,70,26,50,30.8,0.597,21,0
0,93,60,25,92,28.7,0.532,22,0
0,129,80,0,0,31.2,0.703,29,0
5,105,72,29,325,36.9,0.159,28,0
3,128,78,0,0,21.1,0.268,55,0
5,106,82,30,0,39.5,0.286,38,0
2,108,52,26,63,32.5,0.318,22,0
10,108,66,0,0,32.4,0.272,42,1
4,154,62,31,284,32.8,0.237,23,0
0,102,75,23,0,0.0,0.572,21,0
9,57,80,37,0,32.8,0.096,41,0
2,106,64,35,119,30.5,1.4,34,0
5,147,78,0,0,33.7,0.218,65,0
2,90,70,17,0,27.3,0.085,22,0
1,136,74,50,204,37.4,0.399,24,0
4,114,65,0,0,21.9,0.432,37,0
9,156,86,28,155,34.3,1.189,42,1
1,153,82,42,485,40.6,0.687,23,0
8,188,78,0,0,47.9,0.137,43,1
7,152,88,44,0,50.0,0.337,36,1
2,99,52,15,94,24.6,0.637,21,0
1,109,56,21,135,25.2,0.833,23,0
2,88,74,19,53,29.0,0.229,22,0
17,163,72,41,114,40.9,0.817,47,1
4,151,90,38,0,29.7,0.294,36,0
7,102,74,40,105,37.2,0.204,45,0
0,114,80,34,285,44.2,0.167,27,0
2,100,64,23,0,29.7,0.368,21,0
0,131,88,0,0,31.6,0.743,32,1
6,104,74,18,156,29.9,0.722,41,1
3,148,66,25,0,32.5,0.256,22,0
4,120,68,0,0,29.6,0.709,34,0
4,110,66,0,0,31.9,0.471,29,0
3,111,90,12,78,28.4,0.495,29,0
6,102,82,0,0,30.8,0.18,36,1
6,134,70,23,130,35.4,0.542,29,1
2,87,0,23,0,28.9,0.773,25,0
1,79,60,42,48,43.5,0.678,23,0
2,75,64,24,55,29.7,0.37,33,0
8,179,72,42,130,32.7,0.719,36,1
6,85,78,0,0,31.2,0.382,42,0
0,129,110,46,130,67.1,0.319,26,1
5,143,78,0,0,45.0,0.19,47,0
5,130,82,0,0,39.1,0.956,37,1
6,87,80,0,0,23.2,0.084,32,0
0,119,64,18,92,34.9,0.725,23,0
1,0,74,20,23,27.7,0.299,21,0
5,73,60,0,0,26.8,0.268,27,0
4,141,74,0,0,27.6,0.244,40,0
7,194,68,28,0,35.9,0.745,41,1
8,181,68,36,495,30.1,0.615,60,1
1,128,98,41,58,32.0,1.321,33,1
8,109,76,39,114,27.9,0.64,31,1
5,139,80,35,160,31.6,0.361,25,1
3,111,62,0,0,22.6,0.142,21,0
9,123,70,44,94,33.1,0.374,40,0
7,159,66,0,0,30.4,0.383,36,1
11,135,0,0,0,52.3,0.578,40,1
8,85,55,20,0,24.4,0.136,42,0
5,158,84,41,210,39.4,0.395,29,1
1,105,58,0,0,24.3,0.187,21,0
3,107,62,13,48,22.9,0.678,23,1
4,109,64,44,99,34.8,0.905,26,1
4,148,60,27,318,30.9,0.15,29,1
0,113,80,16,0,31.0,0.874,21,0
1,138,82,0,0,40.1,0.236,28,0
0,108,68,20,0,27.3,0.787,32,0
2,99,70,16,44,20.4,0.235,27,0
6,103,72,32,190,37.7,0.324,55,0
5,111,72,28,0,23.9,0.407,27,0
8,196,76,29,280,37.5,0.605,57,1
5,162,104,0,0,37.7,0.151,52,1
1,96,64,27,87,33.2,0.289,21,0
7,184,84,33,0,35.5,0.355,41,1
2,81,60,22,0,27.7,0.29,25,0
0,147,85,54,0,42.8,0.375,24,0
7,179,95,31,0,34.2,0.164,60,0
0,140,65,26,130,42.6,0.431,24,1
9,112,82,32,175,34.2,0.26,36,1
12,151,70,40,271,41.8,0.742,38,1
5,109,62,41,129,35.8,0.514,25,1
6,125,68,30,120,30.0,0.464,32,0
5,85,74,22,0,29.0,1.224,32,1
5,112,66,0,0,37.8,0.261,41,1
0,177,60,29,478,34.6,1.072,21,1
2,158,90,0,0,31.6,0.805,66,1
7,119,0,0,0,25.2,0.209,37,0
7,142,60,33,190,28.8,0.687,61,0
1,100,66,15,56,23.6,0.666,26,0
1,87,78,27,32,34.6,0.101,22,0
0,101,76,0,0,35.7,0.198,26,0
3,162,52,38,0,37.2,0.652,24,1
4,197,70,39,744,36.7,2.329,31,0
0,117,80,31,53,45.2,0.089,24,0
4,142,86,0,0,44.0,0.645,22,1
6,134,80,37,370,46.2,0.238,46,1
1,79,80,25,37,25.4,0.583,22,0
4,122,68,0,0,35.0,0.394,29,0
3,74,68,28,45,29.7,0.293,23,0
4,171,72,0,0,43.6,0.479,26,1
7,181,84,21,192,35.9,0.586,51,1
0,179,90,27,0,44.1,0.686,23,1
9,164,84,21,0,30.8,0.831,32,1
0,104,76,0,0,18.4,0.582,27,0
1,91,64,24,0,29.2,0.192,21,0
4,91,70,32,88,33.1,0.446,22,0
3,139,54,0,0,25.6,0.402,22,1
6,119,50,22,176,27.1,1.318,33,1
2,146,76,35,194,38.2,0.329,29,0
9,184,85,15,0,30.0,1.213,49,1
10,122,68,0,0,31.2,0.258,41,0
0,165,90,33,680,52.3,0.427,23,0
9,124,70,33,402,35.4,0.282,34,0
1,111,86,19,0,30.1,0.143,23,0
9,106,52,0,0,31.2,0.38,42,0
2,129,84,0,0,28.0,0.284,27,0
2,90,80,14,55,24.4,0.249,24,0
0,86,68,32,0,35.8,0.238,25,0
12,92,62,7,258,27.6,0.926,44,1
1,113,64,35,0,33.6,0.543,21,1
3,111,56,39,0,30.1,0.557,30,0
2,114,68,22,0,28.7,0.092,25,0
1,193,50,16,375,25.9,0.655,24,0
11,155,76,28,150,33.3,1.353,51,1
3,191,68,15,130,30.9,0.299,34,0
3,141,0,0,0,30.0,0.761,27,1
4,95,70,32,0,32.1,0.612,24,0
3,142,80,15,0,32.4,0.2,63,0
4,123,62,0,0,32.0,0.226,35,1
5,96,74,18,67,33.6,0.997,43,0
0,138,0,0,0,36.3,0.933,25,1
2,128,64,42,0,40.0,1.101,24,0
0,102,52,0,0,25.1,0.078,21,0
2,146,0,0,0,27.5,0.24,28,1
10,101,86,37,0,45.6,1.136,38,1
2,108,62,32,56,25.2,0.128,21,0
3,122,78,0,0,23.0,0.254,40,0
1,71,78,50,45,33.2,0.422,21,0
13,106,70,0,0,34.2,0.251,52,0
2,100,70,52,57,40.5,0.677,25,0
7,106,60,24,0,26.5,0.296,29,1
0,104,64,23,116,27.8,0.454,23,0
5,114,74,0,0,24.9,0.744,57,0
2,108,62,10,278,25.3,0.881,22,0
0,146,70,0,0,37.9,0.334,28,1
10,129,76,28,122,35.9,0.28,39,0
7,133,88,15,155,32.4,0.262,37,0
7,161,86,0,0,30.4,0.165,47,1
2,108,80,0,0,27.0,0.259,52,1
7,136,74,26,135,26.0,0.647,51,0
5,155,84,44,545,38.7,0.619,34,0
1,119,86,39,220,45.6,0.808,29,1
4,96,56,17,49,20.8,0.34,26,0
5,108,72,43,75,36.1,0.263,33,0
0,78,88,29,40,36.9,0.434,21,0
0,107,62,30,74,36.6,0.757,25,1
2,128,78,37,182,43.3,1.224,31,1
1,128,48,45,194,40.5,0.613,24,1
0,161,50,0,0,21.9,0.254,65,0
6,151,62,31,120,35.5,0.692,28,0
2,146,70,38,360,28.0,0.337,29,1
0,126,84,29,215,30.7,0.52,24,0
14,100,78,25,184,36.6,0.412,46,1
8,112,72,0,0,23.6,0.84,58,0
0,167,0,0,0,32.3,0.839,30,1
2,144,58,33,135,31.6,0.422,25,1
5,77,82,41,42,35.8,0.156,35,0
5,115,98,0,0,52.9,0.209,28,1
3,150,76,0,0,21.0,0.207,37,0
2,120,76,37,105,39.7,0.215,29,0
10,161,68,23,132,25.5,0.326,47,1
0,137,68,14,148,24.8,0.143,21,0
0,128,68,19,180,30.5,1.391,25,1
2,124,68,28,205,32.9,0.875,30,1
6,80,66,30,0,26.2,0.313,41,0
0,106,70,37,148,39.4,0.605,22,0
2,155,74,17,96,26.6,0.433,27,1
3,113,50,10,85,29.5,0.626,25,0
7,109,80,31,0,35.9,1.127,43,1
2,112,68,22,94,34.1,0.315,26,0
3,99,80,11,64,19.3,0.284,30,0
3,182,74,0,0,30.5,0.345,29,1
3,115,66,39,140,38.1,0.15,28,0
6,194,78,0,0,23.5,0.129,59,1
4,129,60,12,231,27.5,0.527,31,0
3,112,74,30,0,31.6,0.197,25,1
0,124,70,20,0,27.4,0.254,36,1
13,152,90,33,29,26.8,0.731,43,1
2,112,75,32,0,35.7,0.148,21,0
1,157,72,21,168,25.6,0.123,24,0
1,122,64,32,156,35.1,0.692,30,1
10,179,70,0,0,35.1,0.2,37,0
2,102,86,36,120,45.5,0.127,23,1
6,105,70,32,68,30.8,0.122,37,0
8,118,72,19,0,23.1,1.476,46,0
2,87,58,16,52,32.7,0.166,25,0
1,180,0,0,0,43.3,0.282,41,1
12,106,80,0,0,23.6,0.137,44,0
1,95,60,18,58,23.9,0.26,22,0
0,165,76,43,255,47.9,0.259,26,0
0,117,0,0,0,33.8,0.932,44,0
5,115,76,0,0,31.2,0.343,44,1
9,152,78,34,171,34.2,0.893,33,1
7,178,84,0,0,39.9,0.331,41,1
1,130,70,13,105,25.9,0.472,22,0
1,95,74,21,73,25.9,0.673,36,0
1,0,68,35,0,32.0,0.389,22,0
5,122,86,0,0,34.7,0.29,33,0
8,95,72,0,0,36.8,0.485,57,0
8,126,88,36,108,38.5,0.349,49,0
1,139,46,19,83,28.7,0.654,22,0
3,116,0,0,0,23.5,0.187,23,0
3,99,62,19,74,21.8,0.279,26,0
5,0,80,32,0,41.0,0.346,37,1
4,92,80,0,0,42.2,0.237,29,0
4,137,84,0,0,31.2,0.252,30,0
3,61,82,28,0,34.4,0.243,46,0
1,90,62,12,43,27.2,0.58,24,0
3,90,78,0,0,42.7,0.559,21,0
9,165,88,0,0,30.4,0.302,49,1
1,125,50,40,167,33.3,0.962,28,1
13,129,0,30,0,39.9,0.569,44,1
12,88,74,40,54,35.3,0.378,48,0
1,196,76,36,249,36.5,0.875,29,1
5,189,64,33,325,31.2,0.583,29,1
5,158,70,0,0,29.8,0.207,63,0
5,103,108,37,0,39.2,0.305,65,0
4,146,78,0,0,38.5,0.52,67,1
4,147,74,25,293,34.9,0.385,30,0
5,99,54,28,83,34.0,0.499,30,0
6,124,72,0,0,27.6,0.368,29,1
0,101,64,17,0,21.0,0.252,21,0
3,81,86,16,66,27.5,0.306,22,0
1,133,102,28,140,32.8,0.234,45,1
3,173,82,48,465,38.4,2.137,25,1
0,118,64,23,89,0.0,1.731,21,0
0,84,64,22,66,35.8,0.545,21,0
2,105,58,40,94,34.9,0.225,25,0
2,122,52,43,158,36.2,0.816,28,0
12,140,82,43,325,39.2,0.528,58,1
0,98,82,15,84,25.2,0.299,22,0
1,87,60,37,75,37.2,0.509,22,0
4,156,75,0,0,48.3,0.238,32,1
0,93,100,39,72,43.4,1.021,35,0
1,107,72,30,82,30.8,0.821,24,0
0,105,68,22,0,20.0,0.236,22,0
1,109,60,8,182,25.4,0.947,21,0
1,90,62,18,59,25.1,1.268,25,0
1,125,70,24,110,24.3,0.221,25,0
1,119,54,13,50,22.3,0.205,24,0
5,116,74,29,0,32.3,0.66,35,1
8,105,100,36,0,43.3,0.239,45,1
5,144,82,26,285,32.0,0.452,58,1
3,100,68,23,81,31.6,0.949,28,0
1,100,66,29,196,32.0,0.444,42,0
5,166,76,0,0,45.7,0.34,27,1
1,131,64,14,415,23.7,0.389,21,0
4,116,72,12,87,22.1,0.463,37,0
4,158,78,0,0,32.9,0.803,31,1
2,127,58,24,275,27.7,1.6,25,0
3,96,56,34,115,24.7,0.944,39,0
0,131,66,40,0,34.3,0.196,22,1
3,82,70,0,0,21.1,0.389,25,0
3,193,70,31,0,34.9,0.241,25,1
4,95,64,0,0,32.0,0.161,31,1
6,137,61,0,0,24.2,0.151,55,0
5,136,84,41,88,35.0,0.286,35,1
9,72,78,25,0,31.6,0.28,38,0
5,168,64,0,0,32.9,0.135,41,1
2,123,48,32,165,42.1,0.52,26,0
4,115,72,0,0,28.9,0.376,46,1
0,101,62,0,0,21.9,0.336,25,0
8,197,74,0,0,25.9,1.191,39,1
1,172,68,49,579,42.4,0.702,28,1
6,102,90,39,0,35.7,0.674,28,0
1,112,72,30,176,34.4,0.528,25,0
1,143,84,23,310,42.4,1.076,22,0
1,143,74,22,61,26.2,0.256,21,0
0,138,60,35,167,34.6,0.534,21,1
3,173,84,33,474,35.7,0.258,22,1
1,97,68,21,0,27.2,1.095,22,0
4,144,82,32,0,38.5,0.554,37,1
1,83,68,0,0,18.2,0.624,27,0
3,129,64,29,115,26.4,0.219,28,1
1,119,88,41,170,45.3,0.507,26,0
2,94,68,18,76,26.0,0.561,21,0
0,102,64,46,78,40.6,0.496,21,0
2,115,64,22,0,30.8,0.421,21,0
8,151,78,32,210,42.9,0.516,36,1
4,184,78,39,277,37.0,0.264,31,1
0,94,0,0,0,0.0,0.256,25,0
1,181,64,30,180,34.1,0.328,38,1
0,135,94,46,145,40.6,0.284,26,0
1,95,82,25,180,35.0,0.233,43,1
2,99,0,0,0,22.2,0.108,23,0
3,89,74,16,85,30.4,0.551,38,0
1,80,74,11,60,30.0,0.527,22,0
2,139,75,0,0,25.6,0.167,29,0
1,90,68,8,0,24.5,1.138,36,0
0,141,0,0,0,42.4,0.205,29,1
12,140,85,33,0,37.4,0.244,41,0
5,147,75,0,0,29.9,0.434,28,0
1,97,70,15,0,18.2,0.147,21,0
6,107,88,0,0,36.8,0.727,31,0
0,189,104,25,0,34.3,0.435,41,1
2,83,66,23,50,32.2,0.497,22,0
4,117,64,27,120,33.2,0.23,24,0
8,108,70,0,0,30.5,0.955,33,1
4,117,62,12,0,29.7,0.38,30,1
0,180,78,63,14,59.4,2.42,25,1
1,100,72,12,70,25.3,0.658,28,0
0,95,80,45,92,36.5,0.33,26,0
0,104,64,37,64,33.6,0.51,22,1
0,120,74,18,63,30.5,0.285,26,0
1,82,64,13,95,21.2,0.415,23,0
2,134,70,0,0,28.9,0.542,23,1
0,91,68,32,210,39.9,0.381,25,0
2,119,0,0,0,19.6,0.832,72,0
2,100,54,28,105,37.8,0.498,24,0
14,175,62,30,0,33.6,0.212,38,1
1,135,54,0,0,26.7,0.687,62,0
5,86,68,28,71,30.2,0.364,24,0
10,148,84,48,237,37.6,1.001,51,1
9,134,74,33,60,25.9,0.46,81,0
9,120,72,22,56,20.8,0.733,48,0
1,71,62,0,0,21.8,0.416,26,0
8,74,70,40,49,35.3,0.705,39,0
5,88,78,30,0,27.6,0.258,37,0
10,115,98,0,0,24.0,1.022,34,0
0,124,56,13,105,21.8,0.452,21,0
0,74,52,10,36,27.8,0.269,22,0
0,97,64,36,100,36.8,0.6,25,0
8,120,0,0,0,30.0,0.183,38,1
6,154,78,41,140,46.1,0.571,27,0
1,144,82,40,0,41.3,0.607,28,0
0,137,70,38,0,33.2,0.17,22,0
0,119,66,27,0,38.8,0.259,22,0
7,136,90,0,0,29.9,0.21,50,0
4,114,64,0,0,28.9,0.126,24,0
0,137,84,27,0,27.3,0.231,59,0
2,105,80,45,191,33.7,0.711,29,1
7,114,76,17,110,23.8,0.466,31,0
8,126,74,38,75,25.9,0.162,39,0
4,132,86,31,0,28.0,0.419,63,0
3,158,70,30,328,35.5,0.344,35,1
0,123,88,37,0,35.2,0.197,29,0
4,85,58,22,49,27.8,0.306,28,0
0,84,82,31,125,38.2,0.233,23,0
0,145,0,0,0,44.2,0.63,31,1
0,135,68,42,250,42.3,0.365,24,1
1,139,62,41,480,40.7,0.536,21,0
0,173,78,32,265,46.5,1.159,58,0
4,99,72,17,0,25.6,0.294,28,0
8,194,80,0,0,26.1,0.551,67,0
2,83,65,28,66,36.8,0.629,24,0
2,89,90,30,0,33.5,0.292,42,0
4,99,68,38,0,32.8,0.145,33,0
4,125,70,18,122,28.9,1.144,45,1
3,80,0,0,0,0.0,0.174,22,0
6,166,74,0,0,26.6,0.304,66,0
5,110,68,0,0,26.0,0.292,30,0
2,81,72,15,76,30.1,0.547,25,0
7,195,70,33,145,25.1,0.163,55,1
6,154,74,32,193,29.3,0.839,39,0
2,117,90,19,71,25.2,0.313,21,0
3,84,72,32,0,37.2,0.267,28,0
6,0,68,41,0,39.0,0.727,41,1
7,94,64,25,79,33.3,0.738,41,0
3,96,78,39,0,37.3,0.238,40,0
10,75,82,0,0,33.3,0.263,38,0
0,180,90,26,90,36.5,0.314,35,1
1,130,60,23,170,28.6,0.692,21,0
2,84,50,23,76,30.4,0.968,21,0
8,120,78,0,0,25.0,0.409,64,0
12,84,72,31,0,29.7,0.297,46,1
0,139,62,17,210,22.1,0.207,21,0
9,91,68,0,0,24.2,0.2,58,0
2,91,62,0,0,27.3,0.525,22,0
3,99,54,19,86,25.6,0.154,24,0
3,163,70,18,105,31.6,0.268,28,1
9,145,88,34,165,30.3,0.771,53,1
7,125,86,0,0,37.6,0.304,51,0
13,76,60,0,0,32.8,0.18,41,0
6,129,90,7,326,19.6,0.582,60,0
2,68,70,32,66,25.0,0.187,25,0
3,124,80,33,130,33.2,0.305,26,0
6,114,0,0,0,0.0,0.189,26,0
9,130,70,0,0,34.2,0.652,45,1
3,125,58,0,0,31.6,0.151,24,0
3,87,60,18,0,21.8,0.444,21,0
1,97,64,19,82,18.2,0.299,21,0
3,116,74,15,105,26.3,0.107,24,0
0,117,66,31,188,30.8,0.493,22,0
0,111,65,0,0,24.6,0.66,31,0
2,122,60,18,106,29.8,0.717,22,0
0,107,76,0,0,45.3,0.686,24,0
1,86,66,52,65,41.3,0.917,29,0
6,91,0,0,0,29.8,0.501,31,0
1,77,56,30,56,33.3,1.251,24,0
4,132,0,0,0,32.9,0.302,23,1
0,105,90,0,0,29.6,0.197,46,0
0,57,60,0,0,21.7,0.735,67,0
0,127,80,37,210,36.3,0.804,23,0
3,129,92,49,155,36.4,0.968,32,1
8,100,74,40,215,39.4,0.661,43,1
3,128,72,25,190,32.4,0.549,27,1
10,90,85,32,0,34.9,0.825,56,1
4,84,90,23,56,39.5,0.159,25,0
1,88,78,29,76,32.0,0.365,29,0
8,186,90,35,225,34.5,0.423,37,1
5,187,76,27,207,43.6,1.034,53,1
4,131,68,21,166,33.1,0.16,28,0
1,164,82,43,67,32.8,0.341,50,0
4,189,110,31,0,28.5,0.68,37,0
1,116,70,28,0,27.4,0.204,21,0
3,84,68,30,106,31.9,0.591,25,0
6,114,88,0,0,27.8,0.247,66,0
1,88,62,24,44,29.9,0.422,23,0
1,84,64,23,115,36.9,0.471,28,0
7,124,70,33,215,25.5,0.161,37,0
1,97,70,40,0,38.1,0.218,30,0
8,110,76,0,0,27.8,0.237,58,0
11,103,68,40,0,46.2,0.126,42,0
11,85,74,0,0,30.1,0.3,35,0
6,125,76,0,0,33.8,0.121,54,1
0,198,66,32,274,41.3,0.502,28,1
1,87,68,34,77,37.6,0.401,24,0
6,99,60,19,54,26.9,0.497,32,0
0,91,80,0,0,32.4,0.601,27,0
2,95,54,14,88,26.1,0.748,22,0
1,99,72,30,18,38.6,0.412,21,0
6,92,62,32,126,32.0,0.085,46,0
4,154,72,29,126,31.3,0.338,37,0
0,121,66,30,165,34.3,0.203,33,1
3,78,70,0,0,32.5,0.27,39,0
2,130,96,0,0,22.6,0.268,21,0
3,111,58,31,44,29.5,0.43,22,0
2,98,60,17,120,34.7,0.198,22,0
1,143,86,30,330,30.1,0.892,23,0
1,119,44,47,63,35.5,0.28,25,0
6,108,44,20,130,24.0,0.813,35,0
2,118,80,0,0,42.9,0.693,21,1
10,133,68,0,0,27.0,0.245,36,0
2,197,70,99,0,34.7,0.575,62,1
0,151,90,46,0,42.1,0.371,21,1
6,109,60,27,0,25.0,0.206,27,0
12,121,78,17,0,26.5,0.259,62,0
8,100,76,0,0,38.7,0.19,42,0
8,124,76,24,600,28.7,0.687,52,1
1,93,56,11,0,22.5,0.417,22,0
8,143,66,0,0,34.9,0.129,41,1
6,103,66,0,0,24.3,0.249,29,0
3,176,86,27,156,33.3,1.154,52,1
0,73,0,0,0,21.1,0.342,25,0
11,111,84,40,0,46.8,0.925,45,1
2,112,78,50,140,39.4,0.175,24,0
3,132,80,0,0,34.4,0.402,44,1
2,82,52,22,115,28.5,1.699,25,0
6,123,72,45,230,33.6,0.733,34,0
0,188,82,14,185,32.0,0.682,22,1
0,67,76,0,0,45.3,0.194,46,0
1,89,24,19,25,27.8,0.559,21,0
1,173,74,0,0,36.8,0.088,38,1
1,109,38,18,120,23.1,0.407,26,0
1,108,88,19,0,27.1,0.4,24,0
6,96,0,0,0,23.7,0.19,28,0
1,124,74,36,0,27.8,0.1,30,0
7,150,78,29,126,35.2,0.692,54,1
4,183,0,0,0,28.4,0.212,36,1
1,124,60,32,0,35.8,0.514,21,0
1,181,78,42,293,40.0,1.258,22,1
1,92,62,25,41,19.5,0.482,25,0
0,152,82,39,272,41.5,0.27,27,0
1,111,62,13,182,24.0,0.138,23,0
3,106,54,21,158,30.9,0.292,24,0
3,174,58,22,194,32.9,0.593,36,1
7,168,88,42,321,38.2,0.787,40,1
6,105,80,28,0,32.5,0.878,26,0
11,138,74,26,144,36.1,0.557,50,1
3,106,72,0,0,25.8,0.207,27,0
6,117,96,0,0,28.7,0.157,30,0
2,68,62,13,15,20.1,0.257,23,0
9,112,82,24,0,28.2,1.282,50,1
0,119,0,0,0,32.4,0.141,24,1
2,112,86,42,160,38.4,0.246,28,0
2,92,76,20,0,24.2,1.698,28,0
6,183,94,0,0,40.8,1.461,45,0
0,94,70,27,115,43.5,0.347,21,0
2,108,64,0,0,30.8,0.158,21,0
4,90,88,47,54,37.7,0.362,29,0
0,125,68,0,0,24.7,0.206,21,0
0,132,78,0,0,32.4,0.393,21,0
5,128,80,0,0,34.6,0.144,45,0
4,94,65,22,0,24.7,0.148,21,0
7,114,64,0,0,27.4,0.732,34,1
0,102,78,40,90,34.5,0.238,24,0
2,111,60,0,0,26.2,0.343,23,0
1,128,82,17,183,27.5,0.115,22,0
10,92,62,0,0,25.9,0.167,31,0
13,104,72,0,0,31.2,0.465,38,1
5,104,74,0,0,28.8,0.153,48,0
2,94,76,18,66,31.6,0.649,23,0
7,97,76,32,91,40.9,0.871,32,1
1,100,74,12,46,19.5,0.149,28,0
0,102,86,17,105,29.3,0.695,27,0
4,128,70,0,0,34.3,0.303,24,0
6,147,80,0,0,29.5,0.178,50,1
4,90,0,0,0,28.0,0.61,31,0
3,103,72,30,152,27.6,0.73,27,0
2,157,74,35,440,39.4,0.134,30,0
1,167,74,17,144,23.4,0.447,33,1
0,179,50,36,159,37.8,0.455,22,1
11,136,84,35,130,28.3,0.26,42,1
0,107,60,25,0,26.4,0.133,23,0
1,91,54,25,100,25.2,0.234,23,0
1,117,60,23,106,33.8,0.466,27,0
5,123,74,40,77,34.1,0.269,28,0
2,120,54,0,0,26.8,0.455,27,0
1,106,70,28,135,34.2,0.142,22,0
2,155,52,27,540,38.7,0.24,25,1
2,101,58,35,90,21.8,0.155,22,0
1,120,80,48,200,38.9,1.162,41,0
11,127,106,0,0,39.0,0.19,51,0
3,80,82,31,70,34.2,1.292,27,1
10,162,84,0,0,27.7,0.182,54,0
1,199,76,43,0,42.9,1.394,22,1
8,167,106,46,231,37.6,0.165,43,1
9,145,80,46,130,37.9,0.637,40,1
6,115,60,39,0,33.7,0.245,40,1
1,112,80,45,132,34.8,0.217,24,0
4,145,82,18,0,32.5,0.235,70,1
10,111,70,27,0,27.5,0.141,40,1
6,98,58,33,190,34.0,0.43,43,0
9,154,78,30,100,30.9,0.164,45,0
6,165,68,26,168,33.6,0.631,49,0
1,99,58,10,0,25.4,0.551,21,0
10,68,106,23,49,35.5,0.285,47,0
3,123,100,35,240,57.3,0.88,22,0
8,91,82,0,0,35.6,0.587,68,0
6,195,70,0,0,30.9,0.328,31,1
9,156,86,0,0,24.8,0.23,53,1
0,93,60,0,0,35.3,0.263,25,0
3,121,52,0,0,36.0,0.127,25,1
2,101,58,17,265,24.2,0.614,23,0
2,56,56,28,45,24.2,0.332,22,0
0,162,76,36,0,49.6,0.364,26,1
0,95,64,39,105,44.6,0.366,22,0
4,125,80,0,0,32.3,0.536,27,1
5,136,82,0,0,0.0,0.64,69,0
2,129,74,26,205,33.2,0.591,25,0
3,130,64,0,0,23.1,0.314,22,0
1,107,50,19,0,28.3,0.181,29,0
1,140,74,26,180,24.1,0.828,23,0
1,144,82,46,180,46.1,0.335,46,1
8,107,80,0,0,24.6,0.856,34,0
13,158,114,0,0,42.3,0.257,44,1
2,121,70,32,95,39.1,0.886,23,0
7,129,68,49,125,38.5,0.439,43,1
2,90,60,0,0,23.5,0.191,25,0
7,142,90,24,480,30.4,0.128,43,1
3,169,74,19,125,29.9,0.268,31,1
0,99,0,0,0,25.0,0.253,22,0
4,127,88,11,155,34.5,0.598,28,0
4,118,70,0,0,44.5,0.904,26,0
2,122,76,27,200,35.9,0.483,26,0
6,125,78,31,0,27.6,0.565,49,1
1,168,88,29,0,35.0,0.905,52,1
2,129,0,0,0,38.5,0.304,41,0
4,110,76,20,100,28.4,0.118,27,0
6,80,80,36,0,39.8,0.177,28,0
10,115,0,0,0,0.0,0.261,30,1
2,127,46,21,335,34.4,0.176,22,0
9,164,78,0,0,32.8,0.148,45,1
2,93,64,32,160,38.0,0.674,23,1
3,158,64,13,387,31.2,0.295,24,0
5,126,78,27,22,29.6,0.439,40,0
10,129,62,36,0,41.2,0.441,38,1
0,134,58,20,291,26.4,0.352,21,0
3,102,74,0,0,29.5,0.121,32,0
7,187,50,33,392,33.9,0.826,34,1
3,173,78,39,185,33.8,0.97,31,1
10,94,72,18,0,23.1,0.595,56,0
1,108,60,46,178,35.5,0.415,24,0
5,97,76,27,0,35.6,0.378,52,1
4,83,86,19,0,29.3,0.317,34,0
1,114,66,36,200,38.1,0.289,21,0
1,149,68,29,127,29.3,0.349,42,1
5,117,86,30,105,39.1,0.251,42,0
1,111,94,0,0,32.8,0.265,45,0
4,112,78,40,0,39.4,0.236,38,0
1,116,78,29,180,36.1,0.496,25,0
0,141,84,26,0,32.4,0.433,22,0
2,175,88,0,0,22.9,0.326,22,0
2,92,52,0,0,30.1,0.141,22,0
3,130,78,23,79,28.4,0.323,34,1
8,120,86,0,0,28.4,0.259,22,1
2,174,88,37,120,44.5,0.646,24,1
2,106,56,27,165,29.0,0.426,22,0
2,105,75,0,0,23.3,0.56,53,0
4,95,60,32,0,35.4,0.284,28,0
0,126,86,27,120,27.4,0.515,21,0
8,65,72,23,0,32.0,0.6,42,0
2,99,60,17,160,36.6,0.453,21,0
1,102,74,0,0,39.5,0.293,42,1
11,120,80,37,150,42.3,0.785,48,1
3,102,44,20,94,30.8,0.4,26,0
1,109,58,18,116,28.5,0.219,22,0
9,140,94,0,0,32.7,0.734,45,1
13,153,88,37,140,40.6,1.174,39,0
12,100,84,33,105,30.0,0.488,46,0
1,147,94,41,0,49.3,0.358,27,1
1,81,74,41,57,46.3,1.096,32,0
3,187,70,22,200,36.4,0.408,36,1
6,162,62,0,0,24.3,0.178,50,1
4,136,70,0,0,31.2,1.182,22,1
1,121,78,39,74,39.0,0.261,28,0
3,108,62,24,0,26.0,0.223,25,0
0,181,88,44,510,43.3,0.222,26,1
8,154,78,32,0,32.4,0.443,45,1
1,128,88,39,110,36.5,1.057,37,1
7,137,90,41,0,32.0,0.391,39,0
0,123,72,0,0,36.3,0.258,52,1
1,106,76,0,0,37.5,0.197,26,0
6,190,92,0,0,35.5,0.278,66,1
2,88,58,26,16,28.4,0.766,22,0
9,170,74,31,0,44.0,0.403,43,1
9,89,62,0,0,22.5,0.142,33,0
10,101,76,48,180,32.9,0.171,63,0
2,122,70,27,0,36.8,0.34,27,0
5,121,72,23,112,26.2,0.245,30,0
1,126,60,0,0,30.1,0.349,47,1
1,93,70,31,0,30.4,0.315,23,0

# encoding: utf-8
import pandas
import matplotlib.pyplot as plt
import seaborn as sns
diabetes = pandas.read_csv('diabetes.csv')
# solution one:
data = diabetes['Outcome'].value_counts()
sns.barplot(x=data.index, y=data.values)
# solution two:
sns.countplot(x='Outcome', data=diabetes)

In this particular problem, I have a very long list of tickers for which I want to retrieve some of the financial information from yahoo finance website using python:
here is the list:
tickers = ["OMKAR.BO", "KCLINFRA.BO", "MERMETL.BO", "PRIMIND.BO", "VISIONCO.BO", "PANAFIC.BO", "KARANWO.BO", "SOURCEIND.BO", "WELCURE.BO", "NAVKETAN.BO", "CUBIFIN.BO", "IMPEXFERRO.BO", "MISHTANN.BO", "SUMERUIND.BO", "MISHTANN.BO", "MADHUVEER.BO", "TNTELE.BO", "JMGCORP.BO", "GSLSEC.BO", "DEVKI.BO", "MINAXI.BO", "INNOCORP.BO", "SURYACHAKRA.BO", "ANKITMETAL.BO", "HAVISHA.BO", "SHIVA.BO", "COMFINTE.BO", "KONNDOR.BO", "PAZEL.BO", "SHARPINV.BO", "MIDINFRA.BO", "UNIVPRIM.BO", "ATHARVENT.BO", "FGP.BO", "BKV.BO", "VIVIDHA.BO", "FISCHER.BO", "ADITRI.BO", "GLFL.BO", "RAJOIL.BO", "ALFL.BO", "PURITY.BO", "ARCEEIN.BO", "INTECH.BO", "MIDEASTP.BO", "STANCAP.BO", "OCTAVE.BO", "TRIJAL.BO", "SREEJAYA.BO", "4THGEN.BO", "RICHIRICH.BO", "VIRTUALS.BO", "SAVINFOCO.BO", "TTIENT.BO", "OONE.BO", "TILAK.BO", "XTGLOBAL.BO", "MANGIND.BO", "ROYALIND.BO", "ASHUTPM.BO", "SMPL.BO", "BGPL.BO", "NYSSACORP.BO", "BILENERGY.BO", "YOGISUNG.BO", "DOLPHMED.BO", "PRATIK.BO", "IPOWER.BO", "BIHSPONG.BO", "CAPFIN.BO", "MCLTD.BO", "KGL.BO", "OMNIAX.BO", "HEERAISP.BO", "VISIONCINE.BO", "SWORDEDGE.BO", "AARVINFRA.BO", "ADVENT.BO", "UVDRHOR.BO", "SUNGOLD.BO", "USHDI.BO", "HINDAPL.BO", "IMEC.BO", "ARAVALIS.BO", "SERVOTEACH.BO", "SCAGRO.BO", "UMESLTD.BO", "CHARMS.BO", "NCLRESE.BO", "SYMBIOX.BO", "PRADIP.BO", "INTEGFD.BO", "CLIOINFO.BO", "RRSECUR.BO", "MUKATPIP.BO", "SYNCOMF.BO", "DYNAMICP.BO", "TRABI.BO", "RADAAN.BO", "KIRANSY-B.BO", "RAMSARUP.BO", "UNIMOVR.BO", "MELSTAR.BO", "OMANSH.BO", "VERTEX.BO", "VENTURA.BO", "GEMSPIN.BO", "EXPLICITFIN.BO", "PASARI.BO", "BABA.BO", "MAHAVIRIND.BO", "BAMPSL.BO", "GAJRA.BO", "SUNRAJDI.BO", "ACCEL.BO", "SIMPLXPAP.BO", "PHARMAID.BO", "JATALIA.BO", "TWINSTAR.BO", "CINDRELL.BO", "SHRGLTR.BO", "EUROMULTI.BO", "CRESSAN.BO", "SEVENHILL.BO", "QUADRANT.BO", "PHTRADING.BO", "SIPTL.BO", "HOTELRUGBY.BO", "KAUSHALYA.BO", "YASHRAJC.BO", "ASHAI.BO", "BERYLSE.BO", "LLOYDSTEEL.BO", "SCANPRO.BO", "HBLEAS.BO", "ASHCAP.BO", "SUNSHINE.BO", "AREALTY.BO", "MSCTC.BO", "HARIAEXPO.BO", "CNIRESLTD.BO", "KABRADG.BO", "CLFL.BO", "TRANSASIA.BO", "KACL.BO", "JAIHINDS.BO", "SANBLUE.BO", "DHENUBUILD.BO", "DHENUBUILD.BO", "ODYCORP.BO", "SAWABUSI.BO", "KAKTEX.BO", "GANONPRO.BO", "GENUSPRIME.BO", "EUREKAI.BO", "CHROMATIC.BO", "ISHWATR.BO", "INTEGRA.BO", "KACL.BO", "SSLFINANCE.BO", "ORIENTTR.BO", "ZHINUDYP.BO", "SWADEIN.BO", "SHKALYN.BO", "BAPACK.BO", "MARUTISE.BO", "PMTELELIN.BO", "SPARCSYS.BO", "GOLKONDA.BO", "DECPO.BO", "NATHUEC.BO", "INDOCITY.BO", "IOSYSTEM.BO", "ADVIKCA.BO", "JRFOODS.BO", "INFOMEDIA.BO", "INDRANIB.BO", "REGTRUS.BO", "RAGHUNAT.BO", "DCMFINSERV.BO", "RRIL.BO", "FILATFASH.BO", "ISWL.BO", "ASINPET.BO", "KORE.BO", "UNIOFFICE.BO", "GUJINV.BO", "QUEST.BO", "GLITTEKG.BO", "AMFORG.BO", "LGBFORGE.BO", "MAL.BO", "CYBERMAT.BO", "AGRIMONY.BO", "METKORE.BO", "SKYLMILAR.BO", "KIRANPR.BO", "RAJSPTR.BO", "SHVFL.BO", "MPFSL.BO", "AMITINT.BO", "KREONFIN.BO", "GRAVITY.BO", "KACHCHH.BO", "STELLANT.BO", "DEVINE.BO", "ICSL.BO", "STELLAR.BO", "CORAGRO.BO", "ARCFIN.BO", "GAMMNINFRA.BO", "EMMSONS.BO", "OSCARGLO.BO", "HARIAAPL.BO", "CORNE.BO", "FACORALL.BO", "KANELIND.BO", "INDOASIAF.BO", "BHANDHOS.BO", "GAGANPO.BO", "SELMCL.BO", "VENLONENT.BO", "KBSINDIA.BO", "RAMAPETRO.BO", "UTIQUE.BO", "GUJSTATFIN.BO", "COUNCODOS.BO", "JDORGOCHEM.BO", "ANSHNCO.BO", "SILVERO.BO", "CONSTRONIC.BO", "SIPIND.BO", "ESARIND.BO", "GUJCOTEX.BO", "HILIKS.BO", "MINFY.BO", "LEENEE.BO", "DUGARHOU.BO", "JHACC.BO", "CINERAD.BO", "GCMCAPI.BO", "GCMCOMM.BO", "CHENFERRO.BO", "MANCREDIT.BO", "TRICOMFRU.BO", "VEGETABLE.BO", "JSHL.BO", "HATHWAYB.BO", "JAYIND.BO", "ROYALCU.BO", "DHANADACO.BO", "ELCIDIN.BO", "RAGHUTOB.BO", "GISOLUTION.BO", "RAGHUTOB.BO", "CONTICON.BO", "NETWORK.BO", "BANASFN.BO", "CRANESSOFT.BO", "RSCINT.BO", "JPTRLES.BO", "ALOKTEXT.BO", "PRAGBOS.BO", "WELTI.BO", "EKAMLEA.BO", "MASL.BO", "SAFFRON.BO", "SRDAPRT.BO", "FFPL.BO", "RITESHIN.BO", "BLOIN.BO", "YARNSYN.BO", "OISL.BO", "POLYTEX.BO", "SPSINT.BO", "GCMCOMM.BO", "FRONTCAP.BO", "SEZAL.BO", "CITYMAN.BO", "AJEL.BO", "ESCORTSFIN.BO", "ABHIINFRA.BO", "PRATIKSH.BO", "JCTLTD.BO", "GENESIS.BO", "HINDSECR.BO", "GKCONS.BO", "MODWOOL.BO", "ROHITFERRO.BO", "NMSRESRC.BO", "VARIMAN.BO", "WAGEND.BO", "INDLEASE.BO", "APOORVA.BO", "HITTCO.BO", "PREMPIPES.BO", "SRMENERGY.BO", "KEDIACN.BO", "TOYAMIND.BO", "EPSOMPRO.BO", "RICHUNV.BO", "CITYONLINE.BO", "ELANGO.BO", "AMITSEC.BO", "CTL.BO", "LPDC.BO", "CONTCHM.BO", "NTL.BO", "SYBLY.BO", "ELEFLOR.BO", "KMFBLDR.BO", "TRIVIKRAMA.BO", "RUCHINFRA.BO", "PROMACT.BO", "USHAKIRA.BO", "ARUNAHTEL.BO", "CIL.BO", "MOUNTSHIQ.BO", "SPTRSHI.BO", "SEATV.BO", "SWASTIVI.BO", "SUNDARAM.BO", "CREATIVEYE.BO", "EUROASIA.BO", "ANJANIFIN.BO", "ADARSH.BO", "GLOBALCA.BO", "INDERGR.BO", "USGTECH.BO", "RASIELEC.BO", "SHEETAL.BO", "SYLPH.BO", "GOYALASS.BO", "KANSAFB.BO", "ANERI.BO", "DRL.BO", "OSWALOR.BO", "SWAGRUHA.BO", "SARTHAKIND.BO", "GALADA.BO", "OSWAYRN.BO", "TRINITYLEA.BO", "GOLCA.BO", "SODFC.BO", "LEADFIN.BO", "KAYPOWR.BO", "PANELEC.BO", "TARAI.BO", "SANJIVIN.BO", "MKTCREAT.BO", "ECOBOAR.BO", "SUNRINV.BO", "MAYURFL.BO", "GARWAMAR.BO", "SURYAKR.BO", "BESTAGRO.BO", "INDCEMCAP.BO", "EASTSILK.BO", "MPAGI.BO", "HRMNYCP.BO", "RUBRAME.BO", "INCON.BO", "AMRAPLIN.BO", "RESPONSINF.BO", "BACPHAR.BO", "KRISHNACAP.BO", "SHBHAWPA.BO", "TOWASOK.BO", "PADMALAYAT.BO", "MHSGRMS.BO", "JMTAUTOLTD.BO", "WELCON.BO", "UNITEDTE.BO", "MNPLFIN.BO", "PARSHINV.BO", "UNISHIRE.BO", "RAJINFRA.BO", "MMLF.BO", "ALCHCORP.BO", "CHMBBRW.BO", "NOGMIND.BO", "SHRMFGC.BO", "SAMTEX.BO", "SUPERTEX.BO", "JAIHINDPRO.BO", "CENTEXT.BO", "BCG.BO", "GENNEX.BO", "EDUCOMP.BO", "SHIVAGR.BO", "ADINATH.BO", "MINID.BO", "SURANAT&P.BO", "GYANDEV.BO", "AVTIL.BO", "ZSWASTSA.BO", "JINDCAP.BO", "NBFOOT.BO", "SHESHAINDS.BO", "UTLINDS.BO", "MADHUSE.BO", "THAMBBI.BO", "KKPLASTICK.BO", "VAGHANI.BO", "SOLIDCO.BO", "HIMFIBP.BO", "KKFIN.BO", "CSL.BO", "GOPAIST.BO", "BALTE.BO", "ETIL.BO", "PAOS.BO", "RAINBOWDQ.BO", "JAGSONFI.BO", "REGENTRP.BO", "AFEL.BO", "BRIPORT.BO", "SURATEX.BO", "INFRAIND.BO", "SPENTEX.BO", "TITANSEC.BO", "ALPSINDUS.BO", "UNISTRMU.BO", "SPECMKT.BO", "SAENTER.BO", "TOKYOFIN.BO", "TRANSFD.BO", "BSELINFRA.BO", "WELSPLSOL.BO", "SONALAD.BO", "CRIMSON.BO", "UNITY.BO", "VIKASPROP.BO", "VELHO.BO", "SYNCOM.BO", "CYBELEIND.BO", "VANICOM.BO", "THAKRAL.BO", "INDOEURO.BO", "ALAN SCOTT.BO", "SALSTEEL.BO", "ADITYA.BO", "HASTIFIN.BO", "NIBE.BO", "JOINTECAED.BO", "GANGAPHARM.BO", "SBECSUG.BO", "EASTBUILD.BO", "LORDSHOTL.BO", "IYKOTHITE.BO", "URJAGLOBA.BO", "DHRUVCA.BO", "RAP.BO", "LAHL.BO", "MONNETIN.BO", "SETUINFRA.BO", "RRMETAL.BO", "GTLINFRA.BO", "ECOM.BO", "TTML.BO", "ARNOLD.BO", "FLORATX.BO", "GARODCH.BO", "PUROHITCON.BO", "KAMRLAB.BO", "MILESTONE.BO", "NETLINK.BO", "MARSONS.BO", "SESL.BO", "OBRSESY.BO", "VRWODAR.BO", "NUWAY.BO", "CJGEL.BO", "REDEXPR.BO", "AISHWARYA.BO", "PICTUREHS.BO", "BAGFILMS.BO", "WOODSVILA.BO", "MEHSECU.BO", "MBPARIKH.BO", "SICLTD.BO", "GITARENEW.BO", "DESHRAK.BO", "SENINFO.BO", "TELECANOR.BO", "STLSTRINF.BO", "JRELTD.BO", "OROSMITHS.BO", "MUNOTHFI.BO", "AVAILFC.BO", "NITINFIRE.BO", "PIFL.BO", "BLBLIMITED.BO", "SRECR.BO", "NAGTECH.BO", "ARISE.BO", "FRONTBUSS.BO", "PAEL.BO", "ROLLT.BO", "VALLABH.BO", "RANASUG.BO", "STRATMONT.BO", "SANTOSHF.BO", "SVAINDIA.BO", "PARKERAC.BO", "VSFPROJ.BO", "AUROCOK.BO", "HKG.BO", "CASTEXTECH.BO", "HOWARHO.BO", "RTNPOWER.BO", "SHRIBCL.BO", "GARWSYN.BO", "MEHSECU.BO", "PRAVEG.BO", "MEHTAHG.BO", "RTNINFRA.BO", "MMWL.BO", "GAGAN.BO", "WWALUM.BO", "HEMANG.BO", "DOLAT.BO", "SUPTANERY.BO", "EUROCERA.BO", "SURFI.BO", "TTIL.BO", "VARDHMAN.BO", "SUPERBAK.BO", "ESHAMEDIA.BO", "CONTILI.BO", "CESL.BO", "DAULAT.BO", "RAJATH.BO", "SURYVANSP.BO", "KUWERIN.BO", "SVARTCORP.BO", "SKRABUL.BO", "WSIND.BO", "DELTA.BO", "SIPL.BO", "RMCHEM.BO", "STDBAT.BO", "PICCASUG.BO", "AGIOPAPER.BO", "SHREYASI.BO", "CCCL.BO", "GAL.BO", "GOLECHA.BO", "RAAJMEDI.BO", "KINETRU.BO", "ZKHANDEN.BO", "LAKHOTIA.BO", "SANINFRA.BO", "KABSON.BO", "ENTRINT.BO", "SIROHIA.BO", "3IINFOTECH.BO", "MEHIF.BO", "BASANTGL.BO", "MAITRI.BO", "CEENIK.BO", "MAXIMAA.BO", "STANPACK.BO", "CRANEINFRA.BO", "CHITRTX.BO", "CAPRICORN.BO", "TAVERNIER.BO", "JPPOWER.BO", "PATIDAR.BO", "BANSTEA.BO", "NEWMKTADV.BO", "DANUBE.BO", "MAHALXSE.BO", "SARDAPPR.BO", "KZLFIN.BO", "ABHIFIN.BO", "AVI.BO", "GAYATRIBI.BO", "VXLINSTR.BO", "ADITYASP.BO", "OMKARPH.BO", "ESSARSEC.BO", "SALSAIN.BO", "NDASEC.BO", "PARABDRUGS.BO", "EPIC.BO", "HIGHSTREE.BO", "TRIMURTHI.BO", "DBSTOCKBRO.BO", "ADARSHPL.BO", "SONAL.BO", "FRASER.BO", "BRIDGESE.BO", "GBGLOBAL.BO", "UNRYLMA.BO", "ANNAINFRA.BO", "RTEXPO.BO", "FUNDVISER.BO", "LIBORD.BO", "HYPERSOFT.BO", "JTAPARIA.BO", "ANUBHAV.BO", "MEGFI.BO", "ACTIONFI.BO", "BCLENTERPR.BO", "RAMSONS.BO", "GUJARATPOLY.BO", "SBFL.BO", "CHDCHEM.BO", "MONEYBOXX.BO", "ALSL.BO", "DEVHARI.BO", "NARPROP.BO", "PIONAGR.BO", "JAYBHCR.BO", "QGO.BO", "KRIFILIND.BO", "GOLDCOINHF.BO", "GALLOPENT.BO", "MIC.BO", "INTELLCAP.BO", "ABIRAFN.BO", "OLPCL.BO", "ZSHERAPR.BO", "CELLA.BO", "ZSANMCOM.BO", "STEELCO.BO", "VFL.BO", "MODAIRY.BO", "ZSANMCOM.BO", "STEELCO.BO", "SHUKJEW.BO", "JAYKAY.BO", "MIC.BO", "MODAIRY.BO", "RGIL.BO", "GSBFIN.BO", "OLPCL.BO", "HINDMOTORS.BO", "GAJANANSEC.BO", "MKEXIM.BO", "BERLDRG.BO", "KUBERJI.BO", "ADDIND.BO", "INDOSOLAR.BO", "GOLDCOINHF.BO", "ACIIN.BO", "UNITINT.BO", "SDC.BO", "RAJKSYN.BO", "CHAMAK.BO", "BHILSPIN.BO", "PANORAMA.BO", "REGAL.BO", "KRRAIL.BO", "AMS.BO", "PARIKSHA.BO", "SURYAINDIA.BO", "ADHARSHILA.BO", "AMARNATH.BO", "JAYATMA.BO", "CANOPYFIN.BO", "FMEC.BO", "CITL.BO", "DAL.BO", "YORKEXP.BO", "MEWATZI.BO"]
and then what I am doing is as below in which I want to get Market Capitalization for each of the tickers in the above list:
from pandas_datareader import data
import pandas as pd
tickers = tick[0:30]
dat = data.get_quote_yahoo(tickers)['marketCap']
print(dat)
I am able to fetch 20-30 tickers using above code, but if I try to pull all, the code throws an error "request timed out" and "list out of range" etc.
Then I tried to fetch data one by one using for loop as below:
f = pd.DataFrame(columns=["Ticker", "MarketCapINR"])
columns = list(df)
for tick in ticks:
dat = data.get_quote_yahoo(tick)['marketCap']
zipped = zip(columns, dat)
a_dictionary = dict(zipped)
df = df.append(a_dictionary, ignore_index=True)
This returned me two errors, one of these is list out of bound and another (when I tried to shorten the length of list using 'slicing'), request timed out.
Is there a way out, to get all the data (ticker names in first column and MarketCap values in second column of a pandas dataframe) ??
..

Here's a solution using a package called yahooquery. Disclaimer: I am the author of the package.
from yahooquery import Ticker
import pandas as pd
tickers = [...] # Use your list above
t = Ticker(tickers)
data = t.quotes
df = pd.DataFrame(data).T
df['marketCap']
OMKAR.BO 4750000
KCLINFRA.BO 26331000
MERMETL.BO 11472136
PRIMIND.BO 22697430
VISIONCO.BO 14777874
...
CANOPYFIN.BO 93859304
FMEC.BO 10542380
CITL.BO NaN
YORKEXP.BO 57503880
MEWATZI.BO 51200000
Name: marketCap, Length: 632, dtype: object

I'm just wondering if there is way to display only the "support" and "confidence" columns? I don't need to display the antecedent, consequent, or lift columns.
This is my code below:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from apyori import apriori
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
dataset = [['plates', 'forks', 'knives'],
['plates', 'bowls', 'glasses'],
['forks', 'knives'],
['glasses', 'forks', 'knives'],
['microwave', 'blender'],
['dumbbell', 'workout bands', 'water bottle'],
['dumbbell', 'sneakers'],
['workout bands', 'sneakers'],
['bike', 'earbuds'],
['yoga mat', 'headphones'],
['camera'],
['iPad', 'earbuds', 'phone charger'],
['iPad', 'laptop', 'laptop charger'],
['headphones', 'laptop', 'laptop charger'],
['iPad', 'bluetooth speaker', 'phone charger'],
['microwave', 'coffee maker'],
['camping tent', 'water bottle', 'flashlight'],
['sleeping bag', 'yoga mat', 'sneakers'],
['tv ', 'tv remote'],
['tv', 'tv remote', 'bluetooth speaker']]
te = TransactionEncoder()
te_ary = te.fit(dataset).transform(dataset)
df = pd.DataFrame(te_ary, columns=te.columns_)
frequent_itemsets = apriori(df, min_support=0.1, use_colnames=True)
frequent_itemsets
association_rules(frequent_itemsets, metric="confidence", min_threshold=0.25)
Thank you!

I do not know the library but the API documentation says the return type of the association_rules is a pandas Dataframe.
So you can do standard pandas stuff with it:
df = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.25)
print(df[["support", "confidence"]])

I am reading a csv file with a CDATE column. The structure of the column is:
|CDATE |
|08/28/2018|
|08/28/2018|
|08/29/2018|
|08/30/2018|
|09/02/2018|
|09/04/2018|
...
|04/10/2019|
As you can see there is duplicate date as well as missing dates in this column, and I would like to find the missing dates and add them to my dataframe.
My code is:
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore")
plt.style.use('fivethirtyeight')
import pandas as pd
df = pd.read_csv("XXX.csv")
dateCol = df['CDATE'].values.tolist()
dates = pd.to_datetime(dateCol, format='%m/%d/%Y')
startDate = dates.min()
endDate = dates.max()
df = df.sort_values('CDATE')
df_plastic = df['PLASTIC'].unique()
dateRange = pd.date_range(startDate, endDate)
df_date = df['CDATE'].unique()
for cursorDate in dateRange:
if (cursorDate in df_date) is False:
print('Data is missing date {} from range {}'.format(cursorDate, df_date))
But the output is:
Data is missing date 2019-02-21 00:00:00 from ['01/01/2019' '01/02/2019' '01/03/2019' '01/04/2019' '01/05/2019'
'01/07/2019' '01/08/2019' '01/09/2019' '01/10/2019' '01/11/2019'
'01/12/2019' '01/14/2019' '01/15/2019' '01/16/2019' '01/17/2019'
'01/18/2019' '01/19/2019' '01/21/2019' '01/22/2019' '01/23/2019'
'01/24/2019' '01/25/2019' '01/26/2019' '01/28/2019' '01/29/2019'
'01/30/2019' '01/31/2019' '02/01/2019' '02/02/2019' '02/04/2019'
'02/05/2019' '02/06/2019' '02/07/2019' '02/08/2019' '02/09/2019'
'02/11/2019' '02/12/2019' '02/13/2019' '02/14/2019' '02/15/2019'
'02/16/2019' '02/19/2019' '02/20/2019' '02/21/2019' '02/22/2019'
'02/23/2019' '02/25/2019' '02/26/2019' '02/27/2019' '02/28/2019'
'03/01/2019' '03/02/2019' '03/03/2019' '03/04/2019' '03/05/2019'
'03/06/2019' '03/07/2019' '03/08/2019' '03/09/2019' '03/11/2019'
'03/12/2019' '03/13/2019' '03/14/2019' '03/15/2019' '03/16/2019'
'03/18/2019' '03/19/2019' '03/20/2019' '03/21/2019' '03/22/2019'
'03/23/2019' '03/25/2019' '03/26/2019' '03/27/2019' '03/28/2019'
'03/29/2019' '03/30/2019' '04/01/2019' '04/02/2019' '04/03/2019'
'04/04/2019' '04/05/2019' '04/06/2019' '04/08/2019' '04/09/2019'
'04/10/2019' '05/29/2018' '05/30/2018' '05/31/2018' '06/01/2018'
'06/02/2018' '06/04/2018' '06/05/2018' '06/06/2018' '06/07/2018'
'06/08/2018' '06/09/2018' '06/11/2018' '06/12/2018' '06/13/2018'
'06/14/2018' '06/15/2018' '06/16/2018' '06/18/2018' '06/19/2018'
'06/20/2018' '06/21/2018' '06/22/2018' '06/23/2018' '06/25/2018'
'06/26/2018' '06/27/2018' '06/28/2018' '06/29/2018' '06/30/2018'
'07/03/2018' '07/04/2018' '07/05/2018' '07/06/2018' '07/07/2018'
'07/09/2018' '07/10/2018' '07/11/2018' '07/12/2018' '07/13/2018'
'07/14/2018' '07/16/2018' '07/17/2018' '07/18/2018' '07/19/2018'
'07/20/2018' '07/21/2018' '07/23/2018' '07/24/2018' '07/25/2018'
'07/26/2018' '07/27/2018' '07/28/2018' '07/30/2018' '07/31/2018'
'08/01/2018' '08/02/2018' '08/03/2018' '08/04/2018' '08/07/2018'
'08/08/2018' '08/09/2018' '08/10/2018' '08/11/2018' '08/13/2018'
'08/14/2018' '08/15/2018' '08/16/2018' '08/17/2018' '08/18/2018'
'08/20/2018' '08/21/2018' '08/22/2018' '08/23/2018' '08/24/2018'
'08/25/2018' '08/27/2018' '08/28/2018' '08/29/2018' '08/30/2018'
'08/31/2018' '09/01/2018' '09/04/2018' '09/05/2018' '09/06/2018'
'09/07/2018' '09/08/2018' '09/10/2018' '09/11/2018' '09/12/2018'
'09/13/2018' '09/14/2018' '09/15/2018' '09/17/2018' '09/18/2018'
'09/19/2018' '09/20/2018' '09/21/2018' '09/22/2018' '09/24/2018'
'09/25/2018' '09/26/2018' '09/27/2018' '09/28/2018' '09/29/2018'
'10/01/2018' '10/02/2018' '10/03/2018' '10/04/2018' '10/05/2018'
'10/06/2018' '10/09/2018' '10/10/2018' '10/11/2018' '10/12/2018'
'10/13/2018' '10/15/2018' '10/16/2018' '10/17/2018' '10/18/2018'
'10/19/2018' '10/20/2018' '10/22/2018' '10/23/2018' '10/24/2018'
'10/25/2018' '10/26/2018' '10/29/2018' '10/30/2018' '10/31/2018'
'11/01/2018' '11/02/2018' '11/03/2018' '11/05/2018' '11/06/2018'
'11/07/2018' '11/08/2018' '11/09/2018' '11/10/2018' '11/13/2018'
'11/14/2018' '11/15/2018' '11/16/2018' '11/18/2018' '11/19/2018'
'11/20/2018' '11/21/2018' '11/22/2018' '11/23/2018' '11/24/2018'
'11/26/2018' '11/27/2018' '11/28/2018' '11/29/2018' '11/30/2018'
'12/01/2018' '12/03/2018' '12/04/2018' '12/05/2018' '12/06/2018'
'12/07/2018' '12/08/2018' '12/09/2018' '12/10/2018' '12/11/2018'
'12/12/2018' '12/13/2018' '12/14/2018' '12/15/2018' '12/17/2018'
'12/18/2018' '12/19/2018' '12/20/2018' '12/21/2018' '12/22/2018'
'12/24/2018' '12/25/2018' '12/27/2018' '12/28/2018' '12/29/2018'
'12/31/2018']
Somehow the data type of cursorDate is changed to Timestamp, making the value comparison not work.
How is it converting the datetime formats?

Building on my comment above. Change the last line before your loop to this:
df_date = df['CDATE'].apply(pd.to_datetime).unique()

I came across a dataset of Twitter users (Kaggle Source) but I have found that the dataset has a rather strange format. It contains a row with column headers, and then rows of what are essentially JSON arrays. The dataset is also quite large which makes it very difficult to convert the entire file into JSON objects.
What is a good way to load this data into Python, preferably a Pandas Dataframe?
Example of Data
id,screenName,tags,avatar,followersCount,friendsCount,lang,lastSeen,tweetId,friends
"1969527638","LlngoMakeEmCum_",[ "#nationaldogday" ],"http://pbs.twimg.com/profile_images/534286217882652672/FNmiQYVO_normal.jpeg",319,112,"en",1472271687519,"769310701580083200",[ "1969574754", "1969295556", "1969284056", "1969612214", "1970067476", "1969797386", "1969430539", "1969840064", "1969698176", "1970005154", "283011644", "1969901029", "1969563175", "1969302314", "1969978662", "1969457936", "1969667533", "1969547821", "1969943478", "1969668032", "283006529", "1969809440", "1969601096", "1969298856", "1969331652", "1969385498", "1969674368", "1969565263", "1970144676", "1969745390", "1969947438", "1969734134", "1969801326", "1969324008", "1969259820", "1969535827", "1970072989", "1969771688", "1969437804", "1969507394", "1969509972", "1969751588", "283012808", "1969302888", "1970224440", "1969603532", "283011244", "1969501046", "1969887518", "1970153138", "1970267527", "1969941955", "1969421654", "1970013110", "1969544905", "1969839590", "1969876500", "1969674625", "1969337952", "1970046536", "1970090934", "1969419133", "1969517215", "1969787869", "1969298065", "1970149771", "1969422638", "1969504268", "1970025554", "1969776001", "1970138611", "1969316186", "1969547558", "1969689272", "283009727", "283015491", "1969526874", "1969662210", "1969536164", "1969320008", "1969893793", "1970158393", "1969365936", "1970194418", "1969942094", "1969631580", "1969704756", "1969920092", "1969712882", "1969791680", "1969408164", "1969754851", "1970205480", "1969840267", "1969443211", "1969706762", "1969692698", "1969751576", "1969486796", "1969286630", "1969686674", "1969833492", "1969294814", "1969472719", "1969685018", "283008559", "283011243", "1969680078", "1969545697", "1969646412", "1969442725", "1969692529" ]
"51878493","_notmichelle",[ "#nationaldogday" ],"http://pbs.twimg.com/profile_images/761977602173046786/4_utEHsD_normal.jpg",275,115,"en",1472270622663,"769309490038439936",[ "60789485", "2420931980", "2899776756", "127410795", "38747286", "1345516880", "236076395", "1242946609", "2567887488", "280777286", "2912446303", "1149916171", "3192577639", "239569380", "229974168", "389097282", "266336410", "1850301204", "2364414805", "812302213", "2318240348", "158634793", "542282350", "569664772", "766573472", "703551325", "168564432", "261054460", "402980453", "562547390", "539630318", "165167145", "22216387", "427568285", "61033129", "213519434", "373092437", "170762012", "273601960", "322108757", "1681816280", "357843027", "737471496", "406541143", "1084122632", "633477616", "537821327", "793079732", "2386380799", "479015607", "783354019", "365171478", "625002575", "2326207404", "1653286842", "1676964216", "2296617326", "1583692190", "1315393903", "377660026", "2235123476", "792779641", "351222527", "444993309", "588396446", "377629159", "469383424", "1726612471", "415230430", "942443390", "360924168", "318593248", "565022085", "319679735", "632508305", "377638254", "1392782078", "584483723", "377703135", "180463340", "564978577", "502517645", "1056960042", "285097108", "410245879", "159121042", "570399371", "502348447", "960927356", "377196638", "478142245", "335043809", "73546116", "11348282", "901302409", "53255593", "515983155", "391774800", "62351523", "724792351", "346296289", "152520627", "559053427", "508019115", "349996133", "378859519", "65120103", "190070557", "339868374", "417355200", "256729771", "16171898", "45266183", "16143507", "165258639" ]

We could start with something like this:
(Might need to rethink the use of | though. We could go for something more exotic like ╡
import pandas as pd
import io
import json
data = '''\
id,screenName,tags,avatar,followersCount,friendsCount,lang,lastSeen,tweetId,friends
"1969527638","LlngoMakeEmCum_",[ "#nationaldogday" ],"http://pbs.twimg.com/profile_images/534286217882652672/FNmiQYVO_normal.jpeg",319,112,"en",1472271687519,"769310701580083200",[ "1969574754", "1969295556", "1969284056", "1969612214", "1970067476", "1969797386", "1969430539", "1969840064", "1969698176", "1970005154", "283011644", "1969901029", "1969563175", "1969302314", "1969978662", "1969457936", "1969667533", "1969547821", "1969943478", "1969668032", "283006529", "1969809440", "1969601096", "1969298856", "1969331652", "1969385498", "1969674368", "1969565263", "1970144676", "1969745390", "1969947438", "1969734134", "1969801326", "1969324008", "1969259820", "1969535827", "1970072989", "1969771688", "1969437804", "1969507394", "1969509972", "1969751588", "283012808", "1969302888", "1970224440", "1969603532", "283011244", "1969501046", "1969887518", "1970153138", "1970267527", "1969941955", "1969421654", "1970013110", "1969544905", "1969839590", "1969876500", "1969674625", "1969337952", "1970046536", "1970090934", "1969419133", "1969517215", "1969787869", "1969298065", "1970149771", "1969422638", "1969504268", "1970025554", "1969776001", "1970138611", "1969316186", "1969547558", "1969689272", "283009727", "283015491", "1969526874", "1969662210", "1969536164", "1969320008", "1969893793", "1970158393", "1969365936", "1970194418", "1969942094", "1969631580", "1969704756", "1969920092", "1969712882", "1969791680", "1969408164", "1969754851", "1970205480", "1969840267", "1969443211", "1969706762", "1969692698", "1969751576", "1969486796", "1969286630", "1969686674", "1969833492", "1969294814", "1969472719", "1969685018", "283008559", "283011243", "1969680078", "1969545697", "1969646412", "1969442725", "1969692529" ]
"51878493","_notmichelle",[ "#nationaldogday" ],"http://pbs.twimg.com/profile_images/761977602173046786/4_utEHsD_normal.jpg",275,115,"en",1472270622663,"769309490038439936",[ "60789485", "2420931980", "2899776756", "127410795", "38747286", "1345516880", "236076395", "1242946609", "2567887488", "280777286", "2912446303", "1149916171", "3192577639", "239569380", "229974168", "389097282", "266336410", "1850301204", "2364414805", "812302213", "2318240348", "158634793", "542282350", "569664772", "766573472", "703551325", "168564432", "261054460", "402980453", "562547390", "539630318", "165167145", "22216387", "427568285", "61033129", "213519434", "373092437", "170762012", "273601960", "322108757", "1681816280", "357843027", "737471496", "406541143", "1084122632", "633477616", "537821327", "793079732", "2386380799", "479015607", "783354019", "365171478", "625002575", "2326207404", "1653286842", "1676964216", "2296617326", "1583692190", "1315393903", "377660026", "2235123476", "792779641", "351222527", "444993309", "588396446", "377629159", "469383424", "1726612471", "415230430", "942443390", "360924168", "318593248", "565022085", "319679735", "632508305", "377638254", "1392782078", "584483723", "377703135", "180463340", "564978577", "502517645", "1056960042", "285097108", "410245879", "159121042", "570399371", "502348447", "960927356", "377196638", "478142245", "335043809", "73546116", "11348282", "901302409", "53255593", "515983155", "391774800", "62351523", "724792351", "346296289", "152520627", "559053427", "508019115", "349996133", "378859519", "65120103", "190070557", "339868374", "417355200", "256729771", "16171898", "45266183", "16143507", "165258639" ]'''
# Create new separator (|) after 9th comma (',')
data = '\n'.join(['|'.join(row.split(',',9)) for row in data.split('\n')])
# REPLACE WITH THIS FOR REAL FILE
#with open('path/to/file') as f:
#data = '\n'.join(['|'.join(row.split(',',9)) for row in f.read().split('\n')])
# Read dataframe
df = pd.read_csv(io.StringIO(data), sep='|')
# Convert strings to objects with json module:
df['friends'] = df['friends'].apply(lambda x: json.loads(x))
df['tags'] = df['tags'].apply(lambda x: json.loads(x))
Safer approach:
import pandas as pd
import io
import json
with open('path/to/file') as f:
columns, *rows = [row.split(',',9) for row in f.read().split('\n')]
df = pd.DataFrame(rows, columns=columns)
# Convert strings to objects with json module:
df['friends'] = df['friends'].apply(lambda x: json.loads(x))
df['tags'] = df['tags'].apply(lambda x: json.loads(x))