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[英]Hiding xticks labels every n-th label or on value on Pandas plot / make x-axis readable
[英]pandas plot xticks on x-axis
我有一個工作代碼,可在圖表中將熊貓數據框顯示為2個折線圖。 我也有一個數據框,可在同一圖表上顯示條形圖。 對於2個數據框,我有x軸的日期。 因為兩個數據幀都有日期,所以我的軸最終只有整數(1,2,3,4,5,6 ...)而不是日期。
我認為這條線df1 = df.set_index(['date'])
指定了我想要的x軸,並且當我df1 = df.set_index(['date'])
圖表上繪制條形圖時,日期顯示的很好,但是當我繪制圖時條形圖,整數顯示在軸上。
我的2個數據框:
df1:
date line1 line2
2015-01-01 15.00 23.00
2015-02-01 18.00 10.00
df2:
date quant
2015-01-01 500
2015-02-01 600
我的代碼:
df1 =pd.DataFrame(result, columns =[ 'date','line1', 'line2'])
df1 = df.set_index(['date'])
df2 =pd.DataFrame(quantity, columns =[ 'quant','date'])
fig = plt.figure()
ax = fig.add_subplot(111)
ax2=ax.twinx()
ax.set_ylim(0,100)
ax2.set_ylim(0,2100)
df1.line1.plot( color = 'red', ax = ax)
df1.line2.plot( color = 'blue', ax = ax)
df2.["quant"].plot(kind = 'bar', ax =ax2, width =0.4)
plt.show()
df1:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 12 entries, 0 to 11
Data columns (total 3 columns):
date 12 non-null object
line1 12 non-null float64
line2 12 non-null float64
dtypes: float64(2), object(1)
memory usage: 384.0+ bytes
None
df2
<class 'pandas.core.frame.DataFrame'>
Int64Index: 11 entries, 0 to 10
Data columns (total 2 columns):
quant 11 non-null int64
date 11 non-null object
dtypes: int64(1), object(1)
memory usage: 264.0+ bytes
None
您可以只使用ax.plot(df1.date, df1.line1)
而matplotlib.pyplot
將自動處理日期。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# your data
# ===================================
np.random.seed(0)
df1 = pd.DataFrame(dict(date=pd.date_range('2015-01-01', periods=12, freq='MS'), line1=np.random.randint(10, 30, 12), line2=np.random.randint(20, 25, 12)))
Out[64]:
date line1 line2
0 2015-01-01 22 22
1 2015-02-01 25 21
2 2015-03-01 10 20
3 2015-04-01 13 21
4 2015-05-01 13 21
5 2015-06-01 17 20
6 2015-07-01 19 21
7 2015-08-01 29 24
8 2015-09-01 28 23
9 2015-10-01 14 20
10 2015-11-01 16 23
11 2015-12-01 22 20
df2 = pd.DataFrame(dict(date=pd.date_range('2015-01-01', periods=12, freq='MS'), quant=100*np.random.randint(3, 10, 12)))
Out[66]:
date quant
0 2015-01-01 500
1 2015-02-01 600
2 2015-03-01 300
3 2015-04-01 400
4 2015-05-01 600
5 2015-06-01 800
6 2015-07-01 600
7 2015-08-01 600
8 2015-09-01 900
9 2015-10-01 300
10 2015-11-01 400
11 2015-12-01 400
# plotting
# ===================================
fig, ax = plt.subplots(figsize=(10, 8))
ax.plot(df1.date, df1.line1, label='line1', c='r')
ax.plot(df1.date, df1.line2, label='line2', c='b')
ax2 = ax.twinx()
ax2.set_ylabel('quant')
ax2.bar(df2.date, df2.quant, width=20, alpha=0.1, color='g', label='quant')
ax.legend(loc='best')
ax.set_xticks(ax.get_xticks()[::2])
后續(更新):
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# your data
# ===================================
np.random.seed(0)
df1 = pd.DataFrame(dict(date=pd.date_range('2015-01-01', periods=12, freq='MS'), line1=np.random.randint(10, 30, 12), line2=np.random.randint(20, 25, 12))).set_index('date')
df2 = pd.DataFrame(dict(date=pd.date_range('2015-01-01', periods=12, freq='MS'), quant=100*np.random.randint(3, 10, 12))).set_index('date')
df2 = df2.drop(df2.index[4])
print(df1)
print(df2)
line1 line2
date
2015-01-01 22 22
2015-02-01 25 21
2015-03-01 10 20
2015-04-01 13 21
2015-05-01 13 21
2015-06-01 17 20
2015-07-01 19 21
2015-08-01 29 24
2015-09-01 28 23
2015-10-01 14 20
2015-11-01 16 23
2015-12-01 22 20
quant
date
2015-01-01 500
2015-02-01 600
2015-03-01 300
2015-04-01 400
2015-06-01 800
2015-07-01 600
2015-08-01 600
2015-09-01 900
2015-10-01 300
2015-11-01 400
2015-12-01 400
# plotting
# ===================================
fig, ax = plt.subplots(figsize=(10, 8))
ax.plot(df1.index, df1.line1, label='line1', c='r')
ax.plot(df1.index, df1.line2, label='line2', c='b')
ax2 = ax.twinx()
ax2.set_ylabel('quant')
ax2.bar(df2.index, df2.quant, width=20, alpha=0.1, color='g', label='quant')
ax.legend(loc='best')
ax.set_xticks(ax.get_xticks()[::2])
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