This class introduces this this this functions
In [5]:
# +, -, *, /
print(5/5)
1.0
In [9]:
my_num = 5.0
In [10]:
#types = int, float, str
type(my_num)
Out[10]:
float
In [24]:
my_name = "Hrant"
my_surname = "Davtyan"
my_full_name = my_name + " " + my_surname
In [25]:
print(my_full_name)
Hrant Davtyan
In [26]:
str(5)
Out[26]:
'5'
In [29]:
int("5")
Out[29]:
5
In [32]:
my_list = [[1],[2]]
In [33]:
type(my_list)
Out[33]:
list
In [35]:
print(my_full_name.upper())
HRANT DAVTYAN
In [36]:
full_name = [my_name,my_surname]
In [40]:
print(full_name[0:1])
['Hrant']
In [41]:
names = ["James","John","Jack","Jimmy"]
In [44]:
print(names[-2])
Jack
In [58]:
names_up = []
for i in names:
names_up.append(i.upper())
In [59]:
names_up = [i.upper() for i in names]
In [61]:
print(names_up)
['JAMES', 'JOHN', 'JACK', 'JIMMY']
In [64]:
#square of interested with a loop and list comprehension
for i in range(1,11):
print(i**2)
1
4
9
16
25
36
49
64
81
100
In [65]:
sqr_list = [i**2 for i in range(1,11)]
In [66]:
print(sqr_list)
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
In [71]:
i=1
while i<11:
print(i**2)
i = i+2
1
9
25
49
81
In [83]:
a = 0
if a>3:
print("Greater")
elif a==3:
print("Equal")
else:
print("Smaller")
Smaller
In [84]:
def superify(text):
return "super"+text
In [88]:
superify("star")
Out[88]:
'superstar'
Libraries
In [89]:
import quandl
In [96]:
data = quandl.get("FRED/DDOE02USA086NWDB")
In [91]:
type(data)
Out[91]:
pandas.core.frame.DataFrame
In [97]:
# take a look to top 3 rows
data.head(3)
Out[97]:
Value
Date
1960-01-01
13.57
1961-01-01
13.72
1962-01-01
13.88
In [93]:
data.tail()
Out[93]:
Value
Date
2018-01-01
4.1
2018-02-01
4.1
2018-03-01
4.1
2018-04-01
3.9
2018-05-01
3.8
In [98]:
import pandas as pd
In [99]:
# head(), tail(), info(), describe()
data.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 56 entries, 1960-01-01 to 2015-01-01
Data columns (total 1 columns):
Value 56 non-null float64
dtypes: float64(1)
memory usage: 896.0 bytes
In [104]:
data.describe()
Out[104]:
Value
count
56.000000
mean
54.911786
std
31.979411
min
13.570000
25%
22.057500
50%
53.170000
75%
81.522500
max
108.700000
In [105]:
df = quandl.get("WIKI/AAPL")
In [106]:
type(df)
Out[106]:
pandas.core.frame.DataFrame
In [107]:
df.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 9400 entries, 1980-12-12 to 2018-03-27
Data columns (total 12 columns):
Open 9400 non-null float64
High 9400 non-null float64
Low 9400 non-null float64
Close 9400 non-null float64
Volume 9400 non-null float64
Ex-Dividend 9400 non-null float64
Split Ratio 9400 non-null float64
Adj. Open 9400 non-null float64
Adj. High 9400 non-null float64
Adj. Low 9400 non-null float64
Adj. Close 9400 non-null float64
Adj. Volume 9400 non-null float64
dtypes: float64(12)
memory usage: 954.7 KB
In [108]:
df.head(3)
Out[108]:
Open
High
Low
Close
Volume
Ex-Dividend
Split Ratio
Adj. Open
Adj. High
Adj. Low
Adj. Close
Adj. Volume
Date
1980-12-12
28.75
28.87
28.75
28.75
2093900.0
0.0
1.0
0.422706
0.424470
0.422706
0.422706
117258400.0
1980-12-15
27.38
27.38
27.25
27.25
785200.0
0.0
1.0
0.402563
0.402563
0.400652
0.400652
43971200.0
1980-12-16
25.37
25.37
25.25
25.25
472000.0
0.0
1.0
0.373010
0.373010
0.371246
0.371246
26432000.0
In [109]:
df.describe()
Out[109]:
Open
High
Low
Close
Volume
Ex-Dividend
Split Ratio
Adj. Open
Adj. High
Adj. Low
Adj. Close
Adj. Volume
count
9400.000000
9400.000000
9400.000000
9400.000000
9.400000e+03
9400.000000
9400.000000
9400.000000
9400.000000
9400.000000
9400.000000
9.400000e+03
mean
101.233437
102.466958
99.896250
101.194472
1.198560e+07
0.003633
1.000957
21.571019
21.774929
21.351252
21.567664
8.860156e+07
std
135.169307
136.294005
133.829528
135.087350
1.662310e+07
0.088269
0.064409
39.272529
39.584888
38.942651
39.271266
8.704777e+07
min
11.120000
11.120000
11.000000
11.000000
4.471000e+03
0.000000
1.000000
0.163495
0.163495
0.161731
0.161731
2.503760e+05
25%
27.000000
27.500000
26.500000
27.000000
1.232075e+06
0.000000
1.000000
0.923453
0.940280
0.904096
0.922730
3.461080e+07
50%
43.750000
44.500000
43.000000
43.880000
3.776200e+06
0.000000
1.000000
1.437461
1.468272
1.410762
1.437445
6.069700e+07
75%
110.120000
111.755000
108.747500
110.060000
1.808312e+07
0.000000
1.000000
20.270182
20.565604
19.905845
20.294924
1.109031e+08
max
702.410000
705.070000
699.570000
702.100000
1.895606e+08
3.290000
7.000000
182.590000
183.500000
180.210000
181.720000
1.855410e+09
In [113]:
#selecting column
opening_pirces_1 = df.Open
opening_pirces_2 = df["Open"]
opening_pirces_3 = df.iloc[:,1]
In [117]:
#rate = 0.86
df["New_Price"] = df*0.86
Out[117]:
Open
High
Low
Close
Volume
Ex-Dividend
Split Ratio
Adj. Open
Adj. High
Adj. Low
Adj. Close
Adj. Volume
New_Price
Date
1980-12-12
24.7250
24.8282
24.725000
24.7250
1800754.00
0.0
0.86
0.363527
0.365044
0.363527
0.363527
1.008422e+08
21.263500
1980-12-15
23.5468
23.5468
23.435000
23.4350
675272.00
0.0
0.86
0.346204
0.346204
0.344560
0.344560
3.781523e+07
20.250248
1980-12-16
21.8182
21.8182
21.715000
21.7150
405920.00
0.0
0.86
0.320789
0.320789
0.319272
0.319272
2.273152e+07
18.763652
1980-12-17
22.2482
22.3600
22.248200
22.2482
331874.00
0.0
0.86
0.327111
0.328755
0.327111
0.327111
1.858494e+07
19.133452
1980-12-18
22.9018
23.0050
22.901800
22.9018
281994.00
0.0
0.86
0.336721
0.338238
0.336721
0.336721
1.579166e+07
19.695548
1980-12-19
24.2950
24.4068
24.295000
24.2950
186706.00
0.0
0.86
0.357205
0.358849
0.357205
0.357205
1.045554e+07
20.893700
1980-12-22
25.4818
25.5850
25.481800
25.4818
143448.00
0.0
0.86
0.374654
0.376172
0.374654
0.374654
8.033088e+06
21.914348
1980-12-23
26.5568
26.6600
26.556800
26.5568
180256.00
0.0
0.86
0.390460
0.391977
0.390460
0.390460
1.009434e+07
22.838848
1980-12-24
27.9500
28.0618
27.950000
27.9500
184298.00
0.0
0.86
0.410944
0.412587
0.410944
0.410944
1.032069e+07
24.037000
1980-12-26
30.5300
30.6332
30.530000
30.5300
213366.00
0.0
0.86
0.448877
0.450394
0.448877
0.448877
1.194850e+07
26.255800
1980-12-29
30.9600
31.0718
30.960000
30.9600
357674.00
0.0
0.86
0.455199
0.456843
0.455199
0.455199
2.002974e+07
26.625600
1980-12-30
30.3150
30.3150
30.203200
30.2032
264450.00
0.0
0.86
0.445716
0.445716
0.444072
0.444072
1.480920e+07
26.070900
1980-12-31
29.4550
29.4550
29.351800
29.3518
137256.00
0.0
0.86
0.433071
0.433071
0.431554
0.431554
7.686336e+06
25.331300
1981-01-02
29.6700
29.8850
29.670000
29.6700
83162.00
0.0
0.86
0.436233
0.439394
0.436233
0.436233
4.657072e+06
25.516200
1981-01-05
29.1282
29.1282
29.025000
29.0250
137170.00
0.0
0.86
0.428267
0.428267
0.426749
0.426749
7.681520e+06
25.050252
1981-01-06
27.8382
27.8382
27.735000
27.7350
173376.00
0.0
0.86
0.409300
0.409300
0.407783
0.407783
9.709056e+06
23.940852
1981-01-07
26.6600
26.6600
26.556800
26.5568
213796.00
0.0
0.86
0.391977
0.391977
0.390460
0.390460
1.197258e+07
22.927600
1981-01-08
26.1182
26.1182
26.015000
26.0150
152908.00
0.0
0.86
0.384011
0.384011
0.382494
0.382494
8.562848e+06
22.461652
1981-01-09
27.4168
27.5200
27.416800
27.4168
82560.00
0.0
0.86
0.403104
0.404621
0.403104
0.403104
4.623360e+06
23.578448
1981-01-12
27.4168
27.4168
27.193200
27.1932
90988.00
0.0
0.86
0.403104
0.403104
0.399817
0.399817
5.095328e+06
23.578448
1981-01-13
26.3418
26.3418
26.230000
26.2300
88494.00
0.0
0.86
0.387299
0.387299
0.385655
0.385655
4.955664e+06
22.653948
1981-01-14
26.3418
26.4450
26.341800
26.3418
54868.00
0.0
0.86
0.387299
0.388816
0.387299
0.387299
3.072608e+06
22.653948
1981-01-15
26.8750
27.0900
26.875000
26.8750
54008.00
0.0
0.86
0.395138
0.398299
0.395138
0.395138
3.024448e+06
23.112500
1981-01-16
26.7632
26.7632
26.660000
26.6600
51428.00
0.0
0.86
0.393494
0.393494
0.391977
0.391977
2.879968e+06
23.016352
1981-01-19
28.2682
28.3800
28.268200
28.2682
159616.00
0.0
0.86
0.415622
0.417266
0.415622
0.415622
8.938496e+06
24.310652
1981-01-20
27.5200
27.5200
27.416800
27.4168
115498.00
0.0
0.86
0.404621
0.404621
0.403104
0.403104
6.467888e+06
23.667200
1981-01-21
27.9500
28.1650
27.950000
27.9500
61060.00
0.0
0.86
0.410944
0.414105
0.410944
0.410944
3.419360e+06
24.037000
1981-01-22
28.2682
28.4918
28.268200
28.2682
136482.00
0.0
0.86
0.415622
0.418910
0.415622
0.415622
7.642992e+06
24.310652
1981-01-23
28.2682
28.3800
28.165000
28.1650
43086.00
0.0
0.86
0.415622
0.417266
0.414105
0.414105
2.412816e+06
24.310652
1981-01-26
27.8382
27.8382
27.735000
27.7350
94600.00
0.0
0.86
0.409300
0.409300
0.407783
0.407783
5.297600e+06
23.940852
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2018-02-13
139.2770
141.6850
139.019000
141.3324
27610090.16
0.0
0.86
139.277000
141.685000
139.019000
141.332400
2.761009e+07
119.778220
2018-02-14
140.2187
144.0844
140.076800
143.9382
34115493.08
0.0
0.86
140.218700
144.084400
140.076800
143.938200
3.411549e+07
120.588082
2018-02-15
146.0194
148.8574
145.340000
148.7714
43524251.70
0.0
0.86
146.019400
148.857400
145.340000
148.771400
4.352425e+07
125.576684
2018-02-16
148.2296
150.3452
147.722200
148.2898
34089361.98
0.0
0.86
148.229600
150.345200
147.722200
148.289800
3.408936e+07
127.477456
2018-02-20
147.9630
149.8636
147.421200
147.7910
28836670.32
0.0
0.86
147.963000
149.863600
147.421200
147.791000
2.883667e+07
127.248180
2018-02-21
148.6338
149.7432
147.068600
147.1202
30816822.04
0.0
0.86
148.633800
149.743200
147.068600
147.120200
3.081682e+07
127.825068
2018-02-22
147.7480
149.5970
147.670600
148.4360
26233539.76
0.0
0.86
147.748000
149.597000
147.670600
148.436000
2.623354e+07
127.063280
2018-02-23
149.3562
151.0590
149.244400
150.9773
28663139.52
0.0
0.86
149.356200
151.059000
149.244400
150.977300
2.866314e+07
128.446332
2018-02-26
151.6610
154.2754
151.540600
153.9142
31722331.52
0.0
0.86
151.661000
154.275400
151.540600
153.914200
3.172233e+07
130.428460
2018-02-27
154.0260
155.2128
153.217600
153.4154
33269241.90
0.0
0.86
154.026000
155.212800
153.217600
153.415400
3.326924e+07
132.462360
2018-02-28
154.1636
155.3289
153.123000
153.1832
28899933.64
0.0
0.86
154.163600
155.328900
153.123000
153.183200
2.889993e+07
132.580696
2018-03-01
153.5444
154.6065
148.487600
150.5000
41969694.20
0.0
0.86
153.544400
154.606500
148.487600
150.500000
4.196969e+07
132.048184
2018-03-02
148.6080
151.6180
148.307000
151.5406
33070397.00
0.0
0.86
148.608000
151.618000
148.307000
151.540600
3.307040e+07
127.802880
2018-03-05
150.6806
152.8564
150.087200
152.0652
24425174.76
0.0
0.86
150.680600
152.856400
150.087200
152.065200
2.442517e+07
129.585316
2018-03-06
153.0026
153.2950
151.471800
151.9362
20458115.16
0.0
0.86
153.002600
153.295000
151.471800
151.936200
2.045812e+07
131.582236
2018-03-07
150.4484
151.2310
149.872200
150.5258
27264977.32
0.0
0.86
150.448400
151.231000
149.872200
150.525800
2.726498e+07
129.385624
2018-03-08
150.9128
152.3232
150.560200
152.1684
19920839.62
0.0
0.86
150.912800
152.323200
150.560200
152.168400
1.992084e+07
129.785008
2018-03-09
153.0456
154.8000
152.555400
154.7828
26991215.24
0.0
0.86
153.045600
154.800000
152.555400
154.782800
2.699122e+07
131.619216
2018-03-12
155.0494
156.8554
154.980600
156.2792
27567648.30
0.0
0.86
155.049400
156.855400
154.980600
156.279200
2.756765e+07
133.342484
2018-03-13
157.0274
157.8100
154.146400
154.7742
26804827.44
0.0
0.86
157.027400
157.810000
154.146400
154.774200
2.680483e+07
135.043564
2018-03-14
155.0752
155.2472
152.916600
153.4584
25004903.34
0.0
0.86
155.075200
155.247200
152.916600
153.458400
2.500490e+07
133.364672
2018-03-15
153.5100
155.0064
153.140286
153.6390
19422725.90
0.0
0.86
153.510000
155.006400
153.140286
153.639000
1.942273e+07
132.018600
2018-03-16
153.6390
154.0432
152.753200
153.0972
31679352.16
0.0
0.86
153.639000
154.043200
152.753200
153.097200
3.167935e+07
132.129540
2018-03-19
152.4952
152.6242
149.347600
150.7580
28212037.70
0.0
0.86
152.495200
152.624200
149.347600
150.758000
2.821204e+07
131.145872
2018-03-20
150.7064
152.0480
150.448400
150.7064
16610073.54
0.0
0.86
150.706400
152.048000
150.448400
150.706400
1.661007e+07
129.607504
2018-03-21
150.5344
150.5774
147.283600
147.2922
30312727.88
0.0
0.86
150.534400
150.577400
147.283600
147.292200
3.031273e+07
129.459584
2018-03-22
146.2000
148.5048
144.996000
145.2067
35303925.36
0.0
0.86
146.200000
148.504800
144.996000
145.206700
3.530393e+07
125.732000
2018-03-23
144.8154
146.1312
141.848400
141.8484
34614100.44
0.0
0.86
144.815400
146.131200
141.848400
141.848400
3.461410e+07
124.541244
2018-03-26
144.5402
148.8660
143.138400
148.5822
31194450.62
0.0
0.86
144.540200
148.866000
143.138400
148.582200
3.119445e+07
124.304572
2018-03-27
149.3648
150.6290
143.551200
144.7724
33508041.54
0.0
0.86
149.364800
150.629000
143.551200
144.772400
3.350804e+07
128.453728
9400 rows × 13 columns
In [118]:
def converter(data):
return data*0.86
In [121]:
df.apply(converter)
Out[121]:
Open
High
Low
Close
Volume
Ex-Dividend
Split Ratio
Adj. Open
Adj. High
Adj. Low
Adj. Close
Adj. Volume
New_Price
Date
1980-12-12
24.7250
24.8282
24.725000
24.7250
1800754.00
0.0
0.86
0.363527
0.365044
0.363527
0.363527
1.008422e+08
21.263500
1980-12-15
23.5468
23.5468
23.435000
23.4350
675272.00
0.0
0.86
0.346204
0.346204
0.344560
0.344560
3.781523e+07
20.250248
1980-12-16
21.8182
21.8182
21.715000
21.7150
405920.00
0.0
0.86
0.320789
0.320789
0.319272
0.319272
2.273152e+07
18.763652
1980-12-17
22.2482
22.3600
22.248200
22.2482
331874.00
0.0
0.86
0.327111
0.328755
0.327111
0.327111
1.858494e+07
19.133452
1980-12-18
22.9018
23.0050
22.901800
22.9018
281994.00
0.0
0.86
0.336721
0.338238
0.336721
0.336721
1.579166e+07
19.695548
1980-12-19
24.2950
24.4068
24.295000
24.2950
186706.00
0.0
0.86
0.357205
0.358849
0.357205
0.357205
1.045554e+07
20.893700
1980-12-22
25.4818
25.5850
25.481800
25.4818
143448.00
0.0
0.86
0.374654
0.376172
0.374654
0.374654
8.033088e+06
21.914348
1980-12-23
26.5568
26.6600
26.556800
26.5568
180256.00
0.0
0.86
0.390460
0.391977
0.390460
0.390460
1.009434e+07
22.838848
1980-12-24
27.9500
28.0618
27.950000
27.9500
184298.00
0.0
0.86
0.410944
0.412587
0.410944
0.410944
1.032069e+07
24.037000
1980-12-26
30.5300
30.6332
30.530000
30.5300
213366.00
0.0
0.86
0.448877
0.450394
0.448877
0.448877
1.194850e+07
26.255800
1980-12-29
30.9600
31.0718
30.960000
30.9600
357674.00
0.0
0.86
0.455199
0.456843
0.455199
0.455199
2.002974e+07
26.625600
1980-12-30
30.3150
30.3150
30.203200
30.2032
264450.00
0.0
0.86
0.445716
0.445716
0.444072
0.444072
1.480920e+07
26.070900
1980-12-31
29.4550
29.4550
29.351800
29.3518
137256.00
0.0
0.86
0.433071
0.433071
0.431554
0.431554
7.686336e+06
25.331300
1981-01-02
29.6700
29.8850
29.670000
29.6700
83162.00
0.0
0.86
0.436233
0.439394
0.436233
0.436233
4.657072e+06
25.516200
1981-01-05
29.1282
29.1282
29.025000
29.0250
137170.00
0.0
0.86
0.428267
0.428267
0.426749
0.426749
7.681520e+06
25.050252
1981-01-06
27.8382
27.8382
27.735000
27.7350
173376.00
0.0
0.86
0.409300
0.409300
0.407783
0.407783
9.709056e+06
23.940852
1981-01-07
26.6600
26.6600
26.556800
26.5568
213796.00
0.0
0.86
0.391977
0.391977
0.390460
0.390460
1.197258e+07
22.927600
1981-01-08
26.1182
26.1182
26.015000
26.0150
152908.00
0.0
0.86
0.384011
0.384011
0.382494
0.382494
8.562848e+06
22.461652
1981-01-09
27.4168
27.5200
27.416800
27.4168
82560.00
0.0
0.86
0.403104
0.404621
0.403104
0.403104
4.623360e+06
23.578448
1981-01-12
27.4168
27.4168
27.193200
27.1932
90988.00
0.0
0.86
0.403104
0.403104
0.399817
0.399817
5.095328e+06
23.578448
1981-01-13
26.3418
26.3418
26.230000
26.2300
88494.00
0.0
0.86
0.387299
0.387299
0.385655
0.385655
4.955664e+06
22.653948
1981-01-14
26.3418
26.4450
26.341800
26.3418
54868.00
0.0
0.86
0.387299
0.388816
0.387299
0.387299
3.072608e+06
22.653948
1981-01-15
26.8750
27.0900
26.875000
26.8750
54008.00
0.0
0.86
0.395138
0.398299
0.395138
0.395138
3.024448e+06
23.112500
1981-01-16
26.7632
26.7632
26.660000
26.6600
51428.00
0.0
0.86
0.393494
0.393494
0.391977
0.391977
2.879968e+06
23.016352
1981-01-19
28.2682
28.3800
28.268200
28.2682
159616.00
0.0
0.86
0.415622
0.417266
0.415622
0.415622
8.938496e+06
24.310652
1981-01-20
27.5200
27.5200
27.416800
27.4168
115498.00
0.0
0.86
0.404621
0.404621
0.403104
0.403104
6.467888e+06
23.667200
1981-01-21
27.9500
28.1650
27.950000
27.9500
61060.00
0.0
0.86
0.410944
0.414105
0.410944
0.410944
3.419360e+06
24.037000
1981-01-22
28.2682
28.4918
28.268200
28.2682
136482.00
0.0
0.86
0.415622
0.418910
0.415622
0.415622
7.642992e+06
24.310652
1981-01-23
28.2682
28.3800
28.165000
28.1650
43086.00
0.0
0.86
0.415622
0.417266
0.414105
0.414105
2.412816e+06
24.310652
1981-01-26
27.8382
27.8382
27.735000
27.7350
94600.00
0.0
0.86
0.409300
0.409300
0.407783
0.407783
5.297600e+06
23.940852
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2018-02-13
139.2770
141.6850
139.019000
141.3324
27610090.16
0.0
0.86
139.277000
141.685000
139.019000
141.332400
2.761009e+07
119.778220
2018-02-14
140.2187
144.0844
140.076800
143.9382
34115493.08
0.0
0.86
140.218700
144.084400
140.076800
143.938200
3.411549e+07
120.588082
2018-02-15
146.0194
148.8574
145.340000
148.7714
43524251.70
0.0
0.86
146.019400
148.857400
145.340000
148.771400
4.352425e+07
125.576684
2018-02-16
148.2296
150.3452
147.722200
148.2898
34089361.98
0.0
0.86
148.229600
150.345200
147.722200
148.289800
3.408936e+07
127.477456
2018-02-20
147.9630
149.8636
147.421200
147.7910
28836670.32
0.0
0.86
147.963000
149.863600
147.421200
147.791000
2.883667e+07
127.248180
2018-02-21
148.6338
149.7432
147.068600
147.1202
30816822.04
0.0
0.86
148.633800
149.743200
147.068600
147.120200
3.081682e+07
127.825068
2018-02-22
147.7480
149.5970
147.670600
148.4360
26233539.76
0.0
0.86
147.748000
149.597000
147.670600
148.436000
2.623354e+07
127.063280
2018-02-23
149.3562
151.0590
149.244400
150.9773
28663139.52
0.0
0.86
149.356200
151.059000
149.244400
150.977300
2.866314e+07
128.446332
2018-02-26
151.6610
154.2754
151.540600
153.9142
31722331.52
0.0
0.86
151.661000
154.275400
151.540600
153.914200
3.172233e+07
130.428460
2018-02-27
154.0260
155.2128
153.217600
153.4154
33269241.90
0.0
0.86
154.026000
155.212800
153.217600
153.415400
3.326924e+07
132.462360
2018-02-28
154.1636
155.3289
153.123000
153.1832
28899933.64
0.0
0.86
154.163600
155.328900
153.123000
153.183200
2.889993e+07
132.580696
2018-03-01
153.5444
154.6065
148.487600
150.5000
41969694.20
0.0
0.86
153.544400
154.606500
148.487600
150.500000
4.196969e+07
132.048184
2018-03-02
148.6080
151.6180
148.307000
151.5406
33070397.00
0.0
0.86
148.608000
151.618000
148.307000
151.540600
3.307040e+07
127.802880
2018-03-05
150.6806
152.8564
150.087200
152.0652
24425174.76
0.0
0.86
150.680600
152.856400
150.087200
152.065200
2.442517e+07
129.585316
2018-03-06
153.0026
153.2950
151.471800
151.9362
20458115.16
0.0
0.86
153.002600
153.295000
151.471800
151.936200
2.045812e+07
131.582236
2018-03-07
150.4484
151.2310
149.872200
150.5258
27264977.32
0.0
0.86
150.448400
151.231000
149.872200
150.525800
2.726498e+07
129.385624
2018-03-08
150.9128
152.3232
150.560200
152.1684
19920839.62
0.0
0.86
150.912800
152.323200
150.560200
152.168400
1.992084e+07
129.785008
2018-03-09
153.0456
154.8000
152.555400
154.7828
26991215.24
0.0
0.86
153.045600
154.800000
152.555400
154.782800
2.699122e+07
131.619216
2018-03-12
155.0494
156.8554
154.980600
156.2792
27567648.30
0.0
0.86
155.049400
156.855400
154.980600
156.279200
2.756765e+07
133.342484
2018-03-13
157.0274
157.8100
154.146400
154.7742
26804827.44
0.0
0.86
157.027400
157.810000
154.146400
154.774200
2.680483e+07
135.043564
2018-03-14
155.0752
155.2472
152.916600
153.4584
25004903.34
0.0
0.86
155.075200
155.247200
152.916600
153.458400
2.500490e+07
133.364672
2018-03-15
153.5100
155.0064
153.140286
153.6390
19422725.90
0.0
0.86
153.510000
155.006400
153.140286
153.639000
1.942273e+07
132.018600
2018-03-16
153.6390
154.0432
152.753200
153.0972
31679352.16
0.0
0.86
153.639000
154.043200
152.753200
153.097200
3.167935e+07
132.129540
2018-03-19
152.4952
152.6242
149.347600
150.7580
28212037.70
0.0
0.86
152.495200
152.624200
149.347600
150.758000
2.821204e+07
131.145872
2018-03-20
150.7064
152.0480
150.448400
150.7064
16610073.54
0.0
0.86
150.706400
152.048000
150.448400
150.706400
1.661007e+07
129.607504
2018-03-21
150.5344
150.5774
147.283600
147.2922
30312727.88
0.0
0.86
150.534400
150.577400
147.283600
147.292200
3.031273e+07
129.459584
2018-03-22
146.2000
148.5048
144.996000
145.2067
35303925.36
0.0
0.86
146.200000
148.504800
144.996000
145.206700
3.530393e+07
125.732000
2018-03-23
144.8154
146.1312
141.848400
141.8484
34614100.44
0.0
0.86
144.815400
146.131200
141.848400
141.848400
3.461410e+07
124.541244
2018-03-26
144.5402
148.8660
143.138400
148.5822
31194450.62
0.0
0.86
144.540200
148.866000
143.138400
148.582200
3.119445e+07
124.304572
2018-03-27
149.3648
150.6290
143.551200
144.7724
33508041.54
0.0
0.86
149.364800
150.629000
143.551200
144.772400
3.350804e+07
128.453728
9400 rows × 13 columns
In [125]:
column_name = input("Pleae, input column name to print ")
print(df[column_name])
Pleae, input column name to print High
Date
1980-12-12 28.870
1980-12-15 27.380
1980-12-16 25.370
1980-12-17 26.000
1980-12-18 26.750
1980-12-19 28.380
1980-12-22 29.750
1980-12-23 31.000
1980-12-24 32.630
1980-12-26 35.620
1980-12-29 36.130
1980-12-30 35.250
1980-12-31 34.250
1981-01-02 34.750
1981-01-05 33.870
1981-01-06 32.370
1981-01-07 31.000
1981-01-08 30.370
1981-01-09 32.000
1981-01-12 31.880
1981-01-13 30.630
1981-01-14 30.750
1981-01-15 31.500
1981-01-16 31.120
1981-01-19 33.000
1981-01-20 32.000
1981-01-21 32.750
1981-01-22 33.130
1981-01-23 33.000
1981-01-26 32.370
...
2018-02-13 164.750
2018-02-14 167.540
2018-02-15 173.090
2018-02-16 174.820
2018-02-20 174.260
2018-02-21 174.120
2018-02-22 173.950
2018-02-23 175.650
2018-02-26 179.390
2018-02-27 180.480
2018-02-28 180.615
2018-03-01 179.775
2018-03-02 176.300
2018-03-05 177.740
2018-03-06 178.250
2018-03-07 175.850
2018-03-08 177.120
2018-03-09 180.000
2018-03-12 182.390
2018-03-13 183.500
2018-03-14 180.520
2018-03-15 180.240
2018-03-16 179.120
2018-03-19 177.470
2018-03-20 176.800
2018-03-21 175.090
2018-03-22 172.680
2018-03-23 169.920
2018-03-26 173.100
2018-03-27 175.150
Name: High, Length: 9400, dtype: float64
In [154]:
my_string = " Please, input column, name to print. "
In [144]:
my_string.replace("Please","Quickly") #replaces old value with new
Out[144]:
'Quickly, input column name to print.'
In [147]:
my_string.strip() #strips down whitespace from the very begining the the very end
Out[147]:
'Please, input column name to print.'
In [152]:
my_string.lower().count("p") #count all p-s whether upper or not
Out[152]:
3
In [162]:
my_string.split(",")[0][1:]
Out[162]:
'Please'
In [164]:
my_string[0:my_string.find(",")]
Out[164]:
' Please'
In [180]:
100USD
In [175]:
splitted = money.split(" ")
In [178]:
print("The user inputed ",splitted[0],"in",splitted[-1])
The user inputed 100 in USD
In [177]:
splitted
Out[177]:
['100', '', '', '', '', '', 'USD']
In [ ]:
["1","0","0","U","S","D"]
[100]
["USD"]
In [182]:
list(money)
Out[182]:
['1', '0', '0', 'U', 'S', 'D']
In [187]:
list(money)[3].isdigit()
Out[187]:
False
In [189]:
#1) split the list
#2) Find intergers and join them
#3) join everying else
In [190]:
In [191]:
print(splitted)
['1', '0', '0', 'U', 'S', 'D']
In [199]:
money = input()
splitted = list(money)
digits = ""
letters = ""
for i in splitted:
if i.isdigit()==True:
digits = digits + i
else:
letters = letters + i
letters = letters.strip()
print("The user inputed ",digits,"in",letters)
100 AMD
The user inputed 100 in AMD
In [200]:
import this
The Zen of Python, by Tim Peters
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
In [ ]:
Content source: HrantDavtyan/Data_Scraping
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