[英]Unable to plot multiple lines in a single graph
I am having a weird issue, I am trying to plot multiple lines in a single graph but it is only one.我有一个奇怪的问题,我试图在一个图中绘制多条线,但它只有一条。 I am sharing the screenshot as you can see the close values are different in both.
我正在分享屏幕截图,因为您可以看到两者的接近值不同。 It is not rendering binance graph as it seems to be overridden.
它没有呈现币安图,因为它似乎被覆盖了。
Graph图形
Update更新
The code is given below代码如下
# All Imports
import ccxt
import pandas as pd
import matplotlib.pyplot as plt
# Connect binance
binance = ccxt.binance()
ftx = ccxt.ftx()
binance_btc_usdt_ohlcv = binance.fetch_ohlcv('BTC/USDT','1d',limit=100)
ftx_btc_usdt_ohlcv = ftx.fetch_ohlcv('BTC/USDT','1d',limit=100)
df_binance = pd.DataFrame(binance_btc_usdt_ohlcv, columns=['ts', 'o', 'h', 'l', 'c', 'v'])
df_ftx = pd.DataFrame(ftx_btc_usdt_ohlcv, columns=['ts', 'o', 'h', 'l', 'c', 'v'])
fig, ax = plt.subplots()
ax.plot(df_binance['ts'], df_binance['v'],label='Binance')
ax.plot(df_ftx['ts'], df_ftx['v'],label='FTX')
plt.legend()
# ax.tick_params(axis='x', colors='red')
plt.show()
python 3.8.12
, pandas 1.3.3
, matplotlib 3.4.3
python 3.8.12
、 pandas 1.3.3
、 matplotlib 3.4.3
'Binance'
is small compared to 'FTX'
, which can be resolved with ax.set_yscale('log')
'Binance'
与'FTX'
相比很小,这可以通过ax.set_yscale('log')
df_binance = pd.DataFrame(binance_btc_usdt_ohlcv, columns=['ts', 'o', 'h', 'l', 'c', 'v'])
df_ftx = pd.DataFrame(ftx_btc_usdt_ohlcv, columns=['ts', 'o', 'h', 'l', 'c', 'v'])
fig, ax = plt.subplots()
ax.plot(df_binance['ts'], df_binance['v'], label='Binance')
ax.plot(df_ftx['ts'], df_ftx['v'], label='FTX')
ax.legend()
ax.set_yscale('log') # resolve issues of scale with the y-axis values
plt.show()
ax.set_yscale('log')
, 'Binance'
still shows up on the plotax.set_yscale('log')
, 'Binance'
仍然出现在情节上'v'
, but the issue was occuring with 'c'
(in the screenshot). 'v'
,但问题出在'c'
(在屏幕截图中)。
df_ftx.c
and df_binance.c
are almost exactly the same, which we can see by using alpha=0.5
.df_ftx.c
和df_binance.c
几乎完全相同,我们可以通过使用alpha=0.5
看到这一点。# plot dataframe
ax = df_binance.plot(x='ts', y='c', label='Binance', figsize=(8, 6), logy=True)
p2 = df_ftx.plot(x='ts', y='c', label='FTX', ax=ax, alpha=0.5)
ax.legend(bbox_to_anchor=(1, 1.02), loc='upper left')
plt.show()
>>> df_binance.c.sub(df_ftx.c)
0 1.00
1 -2.13
2 0.07
3 -2.44
4 -0.35
5 1.35
6 11.51
7 -6.17
8 -11.91
9 -2.86
10 -13.98
11 -7.40
12 -3.13
13 1.56
14 -15.52
15 -8.63
16 0.83
17 10.44
18 0.82
19 -0.95
20 -12.82
21 -2.54
22 -15.13
23 -14.46
24 -4.63
25 -12.60
26 -10.01
27 -17.00
28 -4.00
29 -16.00
30 -9.49
31 -5.18
32 -3.71
33 23.95
34 -4.71
35 -2.38
36 -11.53
37 -7.13
38 -10.78
39 1.85
40 0.01
41 -9.68
42 7.87
43 9.90
44 -4.65
45 2.83
46 5.91
47 -3.11
48 -14.48
49 -11.36
50 -0.86
51 2.64
52 -22.12
53 -8.10
54 -6.27
55 -3.69
56 -0.86
57 1.91
58 5.69
59 1.24
60 -1.27
61 -12.48
62 -1.59
63 -8.18
64 5.98
65 -6.26
66 -4.25
67 -2.38
68 11.38
69 -9.39
70 -4.74
71 -0.43
72 -9.36
73 -3.10
74 -0.65
75 1.54
76 -2.72
77 -1.90
78 -0.39
79 -9.10
80 -4.99
81 -6.06
82 6.99
83 0.00
84 -8.78
85 2.43
86 -2.28
87 -10.00
88 -9.65
89 -5.07
90 -1.00
91 -0.06
92 -28.58
93 -8.43
94 -8.67
95 -17.16
96 -3.41
97 -12.59
98 -1.85
99 5.99
Name: c, dtype: float64
'ts'
to a datetime dtype
with pd.to_datetime
pd.to_datetime
将'ts'
转换为datetime dtype
pd.to_datetime
pandas.DataFrame.plot
since the data is in a dataframepandas.DataFrame.plot
绘图,因为数据在数据pandas.DataFrame.plot
secondary_y
, otherwise use the parameter logy=True
.secondary_y
,否则使用参数logy=True
。df_binance = pd.DataFrame(binance_btc_usdt_ohlcv, columns=['ts', 'o', 'h', 'l', 'c', 'v'])
df_binance.ts = pd.to_datetime(df_binance.ts, unit='ms') # convert column to a datetime dtype
df_ftx = pd.DataFrame(ftx_btc_usdt_ohlcv, columns=['ts', 'o', 'h', 'l', 'c', 'v'])
df_ftx.ts = pd.to_datetime(df_ftx.ts, unit='ms') # convert column to a datetime dtype
# plot dataframe
ax = df_binance.plot(x='ts', y='v', label='Binance', figsize=(8, 6))
p2 = df_ftx.plot(x='ts', y='v', label='FTX', ax=ax, secondary_y=True)
ax.legend(loc='upper left')
p2.legend(loc='upper right')
plt.show()
logy=True
instead of secondary_y=True
logy=True
而不是secondary_y=True
# plot dataframe
ax = df_binance.plot(x='ts', y='v', label='Binance', figsize=(8, 6), logy=True)
p2 = df_ftx.plot(x='ts', y='v', label='FTX', ax=ax)
ax.legend(bbox_to_anchor=(1, 1.02), loc='upper left')
plt.show()
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