In [2]:
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import pandas_datareader.data as web
%matplotlib inline
Can use different plot styles that have already been defined
In [3]:
style.use('ggplot')
In [4]:
start = dt.datetime(2000,1,1)
end = dt.datetime(2016,12,31)
pandas datareader replaced pandas.io.data and is a standalone install
In [5]:
df = web.DataReader('TSLA', 'yahoo', start, end)
In [6]:
df
Out[6]:
Open
High
Low
Close
Volume
Adj Close
Date
2010-06-29
19.000000
25.000000
17.540001
23.889999
18766300
23.889999
2010-06-30
25.790001
30.420000
23.299999
23.830000
17187100
23.830000
2010-07-01
25.000000
25.920000
20.270000
21.959999
8218800
21.959999
2010-07-02
23.000000
23.100000
18.709999
19.200001
5139800
19.200001
2010-07-06
20.000000
20.000000
15.830000
16.110001
6866900
16.110001
2010-07-07
16.400000
16.629999
14.980000
15.800000
6921700
15.800000
2010-07-08
16.139999
17.520000
15.570000
17.459999
7711400
17.459999
2010-07-09
17.580000
17.900000
16.549999
17.400000
4050600
17.400000
2010-07-12
17.950001
18.070000
17.000000
17.049999
2202500
17.049999
2010-07-13
17.389999
18.639999
16.900000
18.139999
2680100
18.139999
2010-07-14
17.940001
20.150000
17.760000
19.840000
4195200
19.840000
2010-07-15
19.940001
21.500000
19.000000
19.889999
3739800
19.889999
2010-07-16
20.700001
21.299999
20.049999
20.639999
2621300
20.639999
2010-07-19
21.370001
22.250000
20.920000
21.910000
2486500
21.910000
2010-07-20
21.850000
21.850000
20.049999
20.299999
1825300
20.299999
2010-07-21
20.660000
20.900000
19.500000
20.219999
1252500
20.219999
2010-07-22
20.500000
21.250000
20.370001
21.000000
957800
21.000000
2010-07-23
21.190001
21.559999
21.059999
21.290001
653600
21.290001
2010-07-26
21.500000
21.500000
20.299999
20.950001
922200
20.950001
2010-07-27
20.910000
21.180000
20.260000
20.549999
619700
20.549999
2010-07-28
20.549999
20.900000
20.510000
20.719999
467200
20.719999
2010-07-29
20.770000
20.879999
20.000000
20.350000
616000
20.350000
2010-07-30
20.200001
20.440001
19.549999
19.940001
426900
19.940001
2010-08-02
20.500000
20.969999
20.330000
20.920000
718100
20.920000
2010-08-03
21.000000
21.950001
20.820000
21.950001
1230500
21.950001
2010-08-04
21.950001
22.180000
20.850000
21.260000
913000
21.260000
2010-08-05
21.540001
21.549999
20.049999
20.450001
796200
20.450001
2010-08-06
20.100000
20.160000
19.520000
19.590000
741900
19.590000
2010-08-09
19.900000
19.980000
19.450001
19.600000
812700
19.600000
2010-08-10
19.650000
19.650000
18.820000
19.030001
1281300
19.030001
...
...
...
...
...
...
...
2016-11-17
183.490005
189.490005
182.110001
188.660004
4777800
188.660004
2016-11-18
190.649994
193.000000
185.000000
185.020004
5201100
185.020004
2016-11-21
185.039993
188.889999
184.410004
184.520004
4344600
184.520004
2016-11-22
185.839996
191.470001
183.710007
191.169998
5600300
191.169998
2016-11-23
190.610001
195.639999
189.000000
193.139999
4885300
193.139999
2016-11-25
193.639999
197.240005
193.639999
196.649994
2363800
196.649994
2016-11-28
195.479996
199.350006
194.550003
196.119995
4487100
196.119995
2016-11-29
195.559998
196.729996
189.500000
189.570007
4431200
189.570007
2016-11-30
191.000000
191.889999
187.500000
189.399994
3535000
189.399994
2016-12-01
188.250000
188.529999
181.000000
181.880005
5112100
181.880005
2016-12-02
182.880005
184.880005
180.000000
181.470001
4037200
181.470001
2016-12-05
182.509995
188.889999
182.509995
186.800003
4066500
186.800003
2016-12-06
185.520004
186.580002
182.679993
185.850006
3372000
185.850006
2016-12-07
186.149994
193.399994
185.000000
193.149994
5441400
193.149994
2016-12-08
192.050003
192.500000
189.539993
192.289993
3187300
192.289993
2016-12-09
190.869995
193.839996
190.809998
192.179993
2719600
192.179993
2016-12-12
192.800003
194.419998
191.179993
192.429993
615800
192.429993
2016-12-13
193.179993
201.279999
193.000000
198.149994
6816100
198.149994
2016-12-14
198.740005
203.000000
196.759995
198.690002
4144600
198.690002
2016-12-15
198.410004
200.740005
197.389999
197.580002
3218200
197.580002
2016-12-16
198.080002
202.589996
197.600006
202.490005
3779800
202.490005
2016-12-19
202.490005
204.449997
199.839996
202.729996
3481500
202.729996
2016-12-20
203.050003
209.000000
202.500000
208.789993
4681400
208.789993
2016-12-21
208.449997
212.229996
207.410004
207.699997
5204800
207.699997
2016-12-22
208.220001
209.990005
206.500000
208.449997
3106900
208.449997
2016-12-23
208.000000
213.449997
207.710007
213.339996
4662900
213.339996
2016-12-27
214.880005
222.250000
214.419998
219.529999
5901400
219.529999
2016-12-28
221.529999
223.800003
217.199997
219.740005
3766900
219.740005
2016-12-29
218.559998
219.199997
214.119995
214.679993
4035900
214.679993
2016-12-30
216.300003
217.500000
211.679993
213.690002
4632700
213.690002
1640 rows × 6 columns
Convert df to csv file
In [7]:
df.to_csv('tsla.csv')
In [8]:
df = pd.read_csv('tsla.csv', parse_dates=True, index_col=0)
In [9]:
df.head()
Out[9]:
Open
High
Low
Close
Volume
Adj Close
Date
2010-06-29
19.000000
25.00
17.540001
23.889999
18766300
23.889999
2010-06-30
25.790001
30.42
23.299999
23.830000
17187100
23.830000
2010-07-01
25.000000
25.92
20.270000
21.959999
8218800
21.959999
2010-07-02
23.000000
23.10
18.709999
19.200001
5139800
19.200001
2010-07-06
20.000000
20.00
15.830000
16.110001
6866900
16.110001
In [16]:
df['High'].plot()
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f3038d0a588>
In [11]:
data = pd.read_csv('masterfile.csv')
In [14]:
data.columns
Out[14]:
Index(['NOMBRE', 'PATERNO', 'MATERNO', 'MUNICIPIO', 'BIMESTRE 1', 'FECHA 1',
'BIMESTRE 2', 'FECHA 2', 'BIMESTRE 3', 'FECHA 3', 'BIMESTRE 4',
'FECHA 4', 'BIMESTRE 5', 'FECHA 5', 'MONTO TOTAL'],
dtype='object')
In [23]:
data.head()
Out[23]:
NOMBRE
PATERNO
MATERNO
MUNICIPIO
BIMESTRE 1
FECHA 1
BIMESTRE 2
FECHA 2
BIMESTRE 3
FECHA 3
BIMESTRE 4
FECHA 4
BIMESTRE 5
FECHA 5
MONTO TOTAL
0
HUMMBERTO ARTURO
OCHOA
HERNANDEZ
ACATIC
2191.2
2016-03-18
2191.2
2016-05-04
2191.2
2016-07-04
2191.2
2016-09-05
2191.2
2016-11-09
10956.0
1
MA GUADALUPE
BALLADARES
ALVARES
ACATIC
2191.2
2016-03-18
2191.2
2016-05-04
2191.2
2016-07-04
2191.2
2016-09-05
2191.2
2016-11-09
10956.0
2
MA ANGELINA
VALLADARES
ALVAREZ
ACATIC
2191.2
2016-03-18
2191.2
2016-05-04
2191.2
2016-07-04
2191.2
2016-09-05
2191.2
2016-11-09
10956.0
3
MARIA
MENDOZA
HERNANDEZ
ACATIC
2191.2
2016-03-18
2191.2
2016-05-04
2191.2
2016-07-04
2191.2
2016-09-05
2191.2
2016-11-09
10956.0
4
REFUJIO
BARRIOS
DE LEON
ACATIC
2191.2
2016-03-18
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2191.2
In [ ]:
In [22]:
data['BIMESTRE 2'][:100].plot()
Out[22]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f3ede191898>
In [ ]:
Content source: 211217613/python_meetup
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