英文:
Combine Binned barplot with lineplot
问题
我想在同一张图上表示两个数据集，一个作为折线图，另一个作为分组条形图。我可以分别做到：但是当我尝试将它们合并时，x轴当然会出现问题：我也似乎无法去掉分组标签。我该如何在同一张图上展示这两个信息呢？
英文:
I'd like to represent two datasets on the same plot, one as a line as one as a binned barplot. I can do each individually:
tobar = pd.melt(pd.DataFrame(np.random.randn(1000).cumsum()))tobar["bins"] = pd.qcut(tobar.index, 20)bp = sns.barplot(data=tobar, x="bins", y="value")
toline = pd.melt(pd.DataFrame(np.random.randn(1000).cumsum()))lp = sns.lineplot(data=toline, x=toline.index, y="value")
But when I try to combine them, of course the x axis gets messed up:
fig, ax = plt.subplots()ax2 = ax.twinx()bp = sns.barplot(data=tobar, x="bins", y="value", ax=ax)lp = sns.lineplot(data=toline, x=toline.index, y="value", ax=ax2)bp.set(xlabel=None)
I also can't seem to get rid of the bin labels.
How can I get these two informations on the one plot?
答案1
得分: 2
- 这个答案解释了为什么最好使用
matplotlib.axes.Axes.bar来绘制柱状图，而不是使用sns.barplot或pandas.DataFrame.bar。- 简而言之，
xtick的位置对应于标签的实际数值，而seaborn和pandas中的xticks是从 0 开始索引的，并不对应实际数值。
- 简而言之，
- 这个答案展示了如何添加柱状图的标签。
- 如果需要，可以对线图使用
ax2 = ax.twinx()。 - 如果线图使用不同的数据也是可以的。
- 在
python 3.11，pandas 1.5.2，matplotlib 3.6.2，seaborn 0.12.1中进行了测试
导入和数据框
import pandas as pdimport seaborn as snsimport matplotlib.pyplot as pltimport numpy as np# 测试数据np.random.seed(2022)df = pd.melt(pd.DataFrame(np.random.randn(1000).cumsum()))# 创建分箱df["bins"] = pd.qcut(df.index, 20)# 添加一个列作为区间的中点df['mid'] = df.bins.apply(lambda row: row.mid.round().astype(int))# 透视数据框以计算每个区间的均值pt = df.pivot_table(index='mid', values='value', aggfunc='mean').reset_index()
绘图 1
# 创建图形fig, ax = plt.subplots(figsize=(30, 7))# 在 y=0 处添加水平线ax.axhline(0, color='black')# 添加柱状图ax.bar(data=pt, x='mid', height='value', width=4, alpha=0.5)# 如果需要，设置 xticks 的标签ax.set_xticks(ticks=pt.mid, labels=pt.mid)# 如果需要，将区间添加为柱状图的标签ax.bar_label(ax.containers[0], labels=df.bins.unique(), weight='bold')# 添加线图_ = sns.lineplot(data=df, x=df.index, y="value", ax=ax, color='tab:orange')
绘图 2
fig, ax = plt.subplots(figsize=(30, 7))ax.axhline(0, color='black')ax.bar(data=pt, x='mid', height='value', width=4, alpha=0.5)ax.set_xticks(ticks=pt.mid, labels=df.bins.unique(), rotation=45)ax.bar_label(ax.containers[0], weight='bold')_ = sns.lineplot(data=df, x=df.index, y="value", ax=ax, color='tab:orange')
绘图 3
- 柱宽为区间的宽度
fig, ax = plt.subplots(figsize=(30, 7))ax.axhline(0, color='black')ax.bar(data=pt, x='mid', height='value', width=50, alpha=0.5, ec='k')ax.set_xticks(ticks=pt.mid, labels=df.bins.unique(), rotation=45)ax.bar_label(ax.containers[0], weight='bold')_ = sns.lineplot(data=df, x=df.index, y="value", ax=ax, color='tab:orange')
英文:
- This answer explains why it's better to plot the bars with
matplotlib.axes.Axes.barinstead ofsns.barplotorpandas.DataFrame.bar.- In short, the
xticklocations correspond to the actual numeric value of the label, whereas thexticksforseabornandpandasare 0 indexed, and don't correspond to the numeric value.
- In short, the
- This answer shows how to add bar labels.
ax2 = ax.twinx()can be used for the line plot if needed- Works the same if the line plot is different data.
- Tested in
python 3.11,pandas 1.5.2,matplotlib 3.6.2,seaborn 0.12.1
Imports and DataFrame
import pandas as pdimport seaborn as snsimport matplotlib.pyplot as pltimport numpy as np# test datanp.random.seed(2022)df = pd.melt(pd.DataFrame(np.random.randn(1000).cumsum()))# create the binsdf["bins"] = pd.qcut(df.index, 20)# add a column for the mid point of the intervaldf['mid'] = df.bins.apply(lambda row: row.mid.round().astype(int))# pivot the dataframe to calculate the mean of each intervalpt = df.pivot_table(index='mid', values='value', aggfunc='mean').reset_index()
Plot 1
# create the figurefig, ax = plt.subplots(figsize=(30, 7))# add a horizontal line at y=0ax.axhline(0, color='black')# add the bar plotax.bar(data=pt, x='mid', height='value', width=4, alpha=0.5)# set the labels on the xticks - if desiredax.set_xticks(ticks=pt.mid, labels=pt.mid)# add the intervals as labels on the bars - if desiredax.bar_label(ax.containers[0], labels=df.bins.unique(), weight='bold')# add the line plot_ = sns.lineplot(data=df, x=df.index, y="value", ax=ax, color='tab:orange')
Plot 2
fig, ax = plt.subplots(figsize=(30, 7))ax.axhline(0, color='black')ax.bar(data=pt, x='mid', height='value', width=4, alpha=0.5)ax.set_xticks(ticks=pt.mid, labels=df.bins.unique(), rotation=45)ax.bar_label(ax.containers[0], weight='bold')_ = sns.lineplot(data=df, x=df.index, y="value", ax=ax, color='tab:orange')
Plot 3
- The bar width is the width of the interval
fig, ax = plt.subplots(figsize=(30, 7))ax.axhline(0, color='black')ax.bar(data=pt, x='mid', height='value', width=50, alpha=0.5, ec='k')ax.set_xticks(ticks=pt.mid, labels=df.bins.unique(), rotation=45)ax.bar_label(ax.containers[0], weight='bold')_ = sns.lineplot(data=df, x=df.index, y="value", ax=ax, color='tab:orange')
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