Python 熊猫从4个数据帧系列中绘制2个子图

我可以从一个数据帧的每个系列中绘制多个子图，如下图所示：

import pandas as pd

NG = [1,2,3,4,5,6,7]
dflist = []

l = [1,2,3,4,5,6]
b = [6,5,4,3,2,1]


for n in NG:
    df = pd.DataFrame(l)
    dflist.append(df)
    df2 = pd.DataFrame(b)
    dflist.append(df2)
    df = pd.concat(dflist, axis = 1)

df.plot(grid = 1, subplots = True, layout = (7,2), sharey = True)

print(df)

import pandas as pd

l = [1,2,3,4,5,6]
b = [6,5,4,3,2,1]

for n in range(7):
    dflist = []
    df = pd.DataFrame(l)
    dflist.append(df)
    df2 = pd.DataFrame(b)
    dflist.append(df2)
    df = pd.concat(dflist, axis = 1)
    ax = subplot(4, 2, n + 1)
    df.plot(ax=ax);

我如何组合每一秒的情节？应该是这样的：

import pandas as pd

NG = [1,2,3,4,5,6,7]
dflist = []

l = [1,2,3,4,5,6]
b = [6,5,4,3,2,1]


for n in NG:
    df = pd.DataFrame(l)
    dflist.append(df)
    df2 = pd.DataFrame(b)
    dflist.append(df2)
    df = pd.concat(dflist, axis = 1)

df.plot(grid = 1, subplots = True, layout = (7,2), sharey = True)

print(df)

import pandas as pd

l = [1,2,3,4,5,6]
b = [6,5,4,3,2,1]

for n in range(7):
    dflist = []
    df = pd.DataFrame(l)
    dflist.append(df)
    df2 = pd.DataFrame(b)
    dflist.append(df2)
    df = pd.concat(dflist, axis = 1)
    ax = subplot(4, 2, n + 1)
    df.plot(ax=ax);

---

如果我正确理解了你的前一个问题和这个问题，那么你正在寻找这样的问题：

import pandas as pd

NG = [1,2,3,4,5,6,7]
dflist = []

l = [1,2,3,4,5,6]
b = [6,5,4,3,2,1]


for n in NG:
    df = pd.DataFrame(l)
    dflist.append(df)
    df2 = pd.DataFrame(b)
    dflist.append(df2)
    df = pd.concat(dflist, axis = 1)

df.plot(grid = 1, subplots = True, layout = (7,2), sharey = True)

print(df)

import pandas as pd

l = [1,2,3,4,5,6]
b = [6,5,4,3,2,1]

for n in range(7):
    dflist = []
    df = pd.DataFrame(l)
    dflist.append(df)
    df2 = pd.DataFrame(b)
    dflist.append(df2)
    df = pd.concat(dflist, axis = 1)
    ax = subplot(4, 2, n + 1)
    df.plot(ax=ax);

---

我得到一个（

TypeError:'tuple'对象不可调用

）带有（

ax=subplot（4，2，n+1）

）。我需要导入Matplotlib吗？Spyder告诉我子绘图不是definedOk，它与matplotlib.pyplot一起工作。是的，这就是我要找的。谢谢vm:-）