pandas 熊猫条形图更改日期格式

Pandas bar plot changes date format

提问by Ted Petrou

I have a simple stacked line plot that has exactly the date format I want magically set when using the following code.

我有一个简单的堆叠线图，它具有使用以下代码时我想要神奇设置的日期格式。

df_ts = df.resample("W", how='max')
df_ts.plot(figsize=(12,8), stacked=True)

However, the dates mysteriously transform themselves to an ugly and unreadable format when plotting the same data as a bar plot.

但是，在绘制与条形图相同的数据时，日期会神秘地转换为丑陋且不可读的格式。

df_ts = df.resample("W", how='max')
df_ts.plot(kind='bar', figsize=(12,8), stacked=True)

The original data was transformed a bit to have the weekly max. Why is this radical change in automatically set dates happening? How can I have the nicely formatted dates as above?

原始数据稍微转换为每周最大值。为什么自动设置日期会发生这种根本性的变化？我怎样才能拥有上述格式良好的日期？

Here is some dummy data

这是一些虚拟数据

start = pd.to_datetime("1-1-2012")
idx = pd.date_range(start, periods= 365).tolist()
df=pd.DataFrame({'A':np.random.random(365), 'B':np.random.random(365)})
df.index = idx
df_ts = df.resample('W', how= 'max')
df_ts.plot(kind='bar', stacked=True)

回答by unutbu

The plotting code assumes that each bar in a bar plot deserves its own label. You could override this assumption by specifying your own formatter:

绘图代码假定条形图中的每个条形都有自己的标签。您可以通过指定自己的格式化程序来覆盖此假设：

ax.xaxis.set_major_formatter(formatter)

The pandas.tseries.converter.TimeSeries_DateFormatterthat Pandas uses to format the dates in the "good" plot works well with line plotswhen the x-values are dates. However, with a bar plotthe x-values (at least those received by TimeSeries_DateFormatter.__call__) are merely integers starting at zero. If you try to use TimeSeries_DateFormatterwith a bar plot, all the labels thus start at the Epoch, 1970-1-1 UTC, since this is the date which corresponds to zero. So the formatter used for line plots is unfortunately useless for bar plots (at least as far as I can see).

该pandas.tseries.converter.TimeSeries_DateFormatter大熊猫使用在“好”的情节设置格式的日期与行之有效线图当X值是日期。但是，对于条形图，x 值（至少由 接收的那些TimeSeries_DateFormatter.__call__）只是从零开始的整数。如果您尝试使用TimeSeries_DateFormatter条形图，则所有标签都从 UTC 时代 1970-1-1 开始，因为这是对应于零的日期。因此，不幸的是，用于线图的格式化程序对条形图毫无用处（至少就我所见）。

The easiest way I see to produce the desired formatting is to generate and set the labels explicitly:

我看到生成所需格式的最简单方法是显式生成和设置标签：

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.ticker as ticker

start = pd.to_datetime("5-1-2012")
idx = pd.date_range(start, periods= 365)
df = pd.DataFrame({'A':np.random.random(365), 'B':np.random.random(365)})
df.index = idx
df_ts = df.resample('W', how= 'max')

ax = df_ts.plot(kind='bar', x=df_ts.index, stacked=True)

# Make most of the ticklabels empty so the labels don't get too crowded
ticklabels = ['']*len(df_ts.index)
# Every 4th ticklable shows the month and day
ticklabels[::4] = [item.strftime('%b %d') for item in df_ts.index[::4]]
# Every 12th ticklabel includes the year
ticklabels[::12] = [item.strftime('%b %d\n%Y') for item in df_ts.index[::12]]
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
plt.gcf().autofmt_xdate()

plt.show()

yields

产量

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For those looking for a simple example of a bar plot with dates:

对于那些正在寻找带有日期的条形图的简单示例的人：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

dates = pd.date_range('2012-1-1', '2017-1-1', freq='M')
df = pd.DataFrame({'A':np.random.random(len(dates)), 'Date':dates})
fig, ax = plt.subplots()
df.plot.bar(x='Date', y='A', ax=ax)
ticklabels = ['']*len(df)
skip = len(df)//12
ticklabels[::skip] = df['Date'].iloc[::skip].dt.strftime('%Y-%m-%d')
ax.xaxis.set_major_formatter(mticker.FixedFormatter(ticklabels))
fig.autofmt_xdate()

# fixes the tracker
# https://matplotlib.org/users/recipes.html
def fmt(x, pos=0, max_i=len(ticklabels)-1):
    i = int(x) 
    i = 0 if i < 0 else max_i if i > max_i else i
    return dates[i]
ax.fmt_xdata = fmt
plt.show()

回答by Arleg

I've struggled with this problem too, and after reading several posts came up with the following solution, which seems to me slightly clearer than matplotlib.datesapproach.

我也一直在努力解决这个问题，在阅读了几篇文章后，提出了以下解决方案，在我看来，这比matplotlib.dates方法更清晰。

Labels without modification:

未修改的标签：

timeline = pd.DatetimeIndex(start='2018, November', freq='M', periods=15)
df = pd.DataFrame({'date': timeline, 'value': np.random.randn(15)})
df.set_index('date', inplace=True)
df.plot(kind='bar', figsize=(12, 8), color='#2ecc71')

Labels with modification:

修改后的标签：

def line_format(label):
    """
    Convert time label to the format of pandas line plot
    """
    month = label.month_name()[:3]
    if month == 'Jan':
        month += f'\n{label.year}'
    return month

# Note that we specify rot here
ax = df.plot(kind='bar', figsize=(12, 8), color='#2ecc71', rot=0)
ax.set_xticklabels(map(lambda x: line_format(x), df.index))

This approach will add year to the label only if it is January

仅当是一月时，此方法才会将年份添加到标签

回答by eecharlie

Here's a possibly easier approach using mdates, though requires you to loop over your columns, calling bar plot from matplotlib. Here's an example where I plot just one column and use mdates for customized ticks and labels (EDITAdded looping function to plot all columns stacked):

这是使用 的一种可能更简单的方法mdates，但需要您遍历列，从 matplotlib 调用条形图。这是一个示例，我只绘制一列并使用 mdates 自定义刻度和标签（编辑添加了循环功能以绘制堆叠的所有列）：

import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

def format_x_date_month_day(ax):   
    # Standard date x-axis formatting block, labels each month and ticks each day
    days = mdates.DayLocator()
    months = mdates.MonthLocator()  # every month
    dayFmt = mdates.DateFormatter('%D')
    monthFmt = mdates.DateFormatter('%Y-%m')
    ax.figure.autofmt_xdate()
    ax.xaxis.set_major_locator(months) 
    ax.xaxis.set_major_formatter(monthFmt)
    ax.xaxis.set_minor_locator(days)

def df_stacked_bar_formattable(df, ax, **kwargs):
    P = []
    lastBar = None

    for col in df.columns:
        X = df.index
        Y = df[col]
        if lastBar is not None:
            P.append(ax.bar(X, Y, bottom=lastBar, **kwargs))
        else:
            P.append(ax.bar(X, Y, **kwargs))
        lastBar = Y
    plt.legend([p[0] for p in P], df.columns)

span_days = 90
start = pd.to_datetime("1-1-2012")
idx = pd.date_range(start, periods=span_days).tolist()
df=pd.DataFrame(index=idx, data={'A':np.random.random(span_days), 'B':np.random.random(span_days)})

plt.close('all')
fig, ax = plt.subplots(1)
df_stacked_bar_formattable(df, ax)
format_x_date_month_day(ax)
plt.show()

(Referencing matplotlib.orgfor example of looping to create a stacked bar plot.) This gives us

（参考matplotlib.org例如循环创建堆叠条形图。）这给了我们

Another approach that shouldwork and be much easier is to use df.plot.bar(ax=ax, stacked=True), however it does not admit date axis formatting with mdatesand is the subject of my question.

另一种应该工作并且更容易使用的方法是使用df.plot.bar(ax=ax, stacked=True)，但是它不允许日期轴格式化，mdates并且是我的问题的主题。

回答by Daniel R

Maybe not the most elegant, but hopefully easy way:

也许不是最优雅的，但希望是简单的方法：

fig = plt.figure() 
ax = fig.add_subplot(111)

df_ts.plot(kind='bar', figsize=(12,8), stacked=True,ax=ax)
ax.set_xticklabels(''*len(df_ts.index))

df_ts.plot(linewidth=0, ax=ax)  # This sets the nice x_ticks automatically

[EDIT]: ax=ax neede in df_ts.plot()

[编辑]：df_ts.plot() 中的 ax=ax 需要