如何绘制元素计数并添加注释
How to plot element count and add annotations
我在“globalterrorism.csv”工作，想想象每个国家的恐怖袭击。我也是这样做的：
len(gt['country_txt'].unique())
我得到了 205 个独特的国家。
labels = gt.groupby(["country_txt"]).count()['eventid'].index
x = np.array(list(gt.groupby(["country_txt"]).count()['eventid']))
而 x 是：
x = np.array([12629, 80, 2690, 1, 492, 1, 776, 23, 113, 112, 48, 5, 207, 1636, 3, 13, 151, 5, 8, 6, 309, 159, 10, 253, 6, 51, 52, 610, 248, 330, 94, 283, 87, 2352, 248, 7922, 5, 67, 57, 30, 130, 32, 10, 759, 41, 22, 3, 87, 38, 10, 210, 2479, 5274, 2, 9, 16, 167, 1, 17, 20, 2678, 7, 3, 8, 3, 206, 728, 19, 1268, 5, 56, 1984, 23, 8, 26, 212, 317, 26, 46, 4, 11748, 752, 1, 677, 24553, 299, 2171, 1536, 72, 35, 399, 107, 26, 677, 194, 73, 32, 27, 17, 2417, 28, 28, 2249, 8, 16, 33, 116, 25, 5, 95, 21, 561, 23, 12, 17, 2, 497, 21, 5, 36, 355, 543, 150, 1194, 121, 30, 1, 19, 1949, 148, 3864, 1, 6, 19, 14261, 126, 86, 113, 3, 6050, 6761, 39, 140, 7, 36, 83, 6, 2143, 157, 371, 117, 12, 11, 2, 87, 7, 18, 6, 4, 4093, 2015, 38, 225, 1, 2, 75, 3208, 2998, 2, 1, 942, 65, 16, 131, 107, 2198, 49, 181, 58, 3836, 48, 22, 107, 4260, 2, 390, 1708, 21, 5222, 2824, 76, 20, 2, 1, 273, 12, 1, 2196, 537, 4, 3309, 200, 50, 62, 100])
-len为205
ax = plt.figure(figsize = (12,7))
plt.hist(x, bins = 205)
这是结果：
但我需要绘制 'x' 的所有值，并针对每个值绘制标签！！
谁能告诉我该怎么做？
- 使用来自 data.world: Global Terrorism Data 的数据。 Select 要下载的文件，然后选择 下载所有文件 选项。将文件解压缩到默认文件夹名称中。
- 总数据集为
157520 rows × 137 columns
- 将
kind 的数据绘制为 'bar' 或 'barh'，而不是 'hist'
- 而不是
.groupby，使用 .value_counts 将简化代码。
- 用
ax.bar_label注释。请参阅 Adding value labels on a matplotlib bar chart 了解更多注释信息
- 如果升级
matplotlib 不是一个选项，请使用 Adding value labels on a matplotlib bar chart 中显示的旧注释方法。
- 使用
pandas v1.2.4 和 matplotlib v3.4.2。
import pandas as pd
from pathlib import Path
# find the files
files = Path('data-society-global-terrorism-data/').glob('*.csv')
# create a single dataframe of the 4 csv files
gt = pd.concat([pd.read_csv(file, low_memory=False) for file in files]).reset_index(drop=True)
# display(gt.loc[:5, ['country_txt', 'eventid']]
country_txt eventid
0 Northern Ireland 199301010001
1 Egypt 199301010002
2 Sri Lanka 199301030001
3 Israel 199301030002
4 Israel 199301030003
5 Northern Ireland 199301030004
# groupby and get the top 20
# gtg = gt.groupby(["country_txt"])['eventid'].count().sort_values(ascending=False).head(20)
# optional: using value_counts is easier
gtg = gt.country_txt.value_counts().head(20)
# plot the figure as a bar plot, not a histogram
ax = gtg.plot(kind='barh', figsize=(7, 12))
# annotate the bars
ax.bar_label(ax.containers[-1], label_type='edge')
# bump out the x margin so the annotation doesn't stick out
ax.margins(x=0.1)
两列示例数据
data = {'country_txt': ['Colombia', 'Afghanistan', 'Colombia', 'Colombia', 'Pakistan', 'India', 'India', 'Colombia', 'India', 'Colombia', 'Colombia', 'Colombia', 'Colombia', 'Colombia', 'Colombia', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'Pakistan', 'India', 'India', 'India', 'India', 'India', 'Colombia', 'Colombia', 'Colombia', 'Afghanistan', 'India', 'Pakistan', 'Colombia', 'India', 'India', 'India', 'Pakistan', 'Pakistan', 'Pakistan', 'Colombia', 'India', 'India', 'India', 'Colombia', 'Colombia', 'India', 'Pakistan', 'India', 'India', 'India', 'India', 'Pakistan', 'Colombia', 'Colombia', 'Colombia', 'India', 'India', 'Colombia', 'Afghanistan', 'Iraq', 'India', 'India', 'India', 'India', 'Iraq', 'Iraq', 'Pakistan', 'Pakistan', 'Pakistan', 'Pakistan', 'Iraq', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Iraq', 'Pakistan', 'Iraq', 'Iraq', 'India', 'Pakistan', 'Pakistan', 'Afghanistan', 'India', 'Afghanistan', 'Iraq', 'Iraq', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Iraq', 'Pakistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Pakistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Afghanistan', 'India', 'Pakistan', 'Iraq', 'Iraq', 'Iraq', 'Pakistan', 'Afghanistan', 'Pakistan', 'Pakistan', 'Pakistan', 'Iraq', 'Afghanistan', 'Afghanistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'India', 'Iraq', 'Iraq', 'Afghanistan', 'India', 'Pakistan', 'Afghanistan', 'Pakistan', 'Pakistan', 'Pakistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Pakistan', 'Pakistan', 'Pakistan', 'Afghanistan', 'Pakistan', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Afghanistan', 'Pakistan', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Afghanistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'India', 'Afghanistan', 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gt = pd.DataFrame(data)
我在“globalterrorism.csv”工作，想想象每个国家的恐怖袭击。我也是这样做的：
len(gt['country_txt'].unique())
我得到了 205 个独特的国家。
labels = gt.groupby(["country_txt"]).count()['eventid'].index
x = np.array(list(gt.groupby(["country_txt"]).count()['eventid']))
而 x 是：
x = np.array([12629, 80, 2690, 1, 492, 1, 776, 23, 113, 112, 48, 5, 207, 1636, 3, 13, 151, 5, 8, 6, 309, 159, 10, 253, 6, 51, 52, 610, 248, 330, 94, 283, 87, 2352, 248, 7922, 5, 67, 57, 30, 130, 32, 10, 759, 41, 22, 3, 87, 38, 10, 210, 2479, 5274, 2, 9, 16, 167, 1, 17, 20, 2678, 7, 3, 8, 3, 206, 728, 19, 1268, 5, 56, 1984, 23, 8, 26, 212, 317, 26, 46, 4, 11748, 752, 1, 677, 24553, 299, 2171, 1536, 72, 35, 399, 107, 26, 677, 194, 73, 32, 27, 17, 2417, 28, 28, 2249, 8, 16, 33, 116, 25, 5, 95, 21, 561, 23, 12, 17, 2, 497, 21, 5, 36, 355, 543, 150, 1194, 121, 30, 1, 19, 1949, 148, 3864, 1, 6, 19, 14261, 126, 86, 113, 3, 6050, 6761, 39, 140, 7, 36, 83, 6, 2143, 157, 371, 117, 12, 11, 2, 87, 7, 18, 6, 4, 4093, 2015, 38, 225, 1, 2, 75, 3208, 2998, 2, 1, 942, 65, 16, 131, 107, 2198, 49, 181, 58, 3836, 48, 22, 107, 4260, 2, 390, 1708, 21, 5222, 2824, 76, 20, 2, 1, 273, 12, 1, 2196, 537, 4, 3309, 200, 50, 62, 100])
-len为205
ax = plt.figure(figsize = (12,7))
plt.hist(x, bins = 205)
这是结果：
但我需要绘制 'x' 的所有值，并针对每个值绘制标签！！
谁能告诉我该怎么做？
- 使用来自 data.world: Global Terrorism Data 的数据。 Select 要下载的文件，然后选择 下载所有文件 选项。将文件解压缩到默认文件夹名称中。
- 总数据集为
157520 rows × 137 columns
- 总数据集为
- 将
kind的数据绘制为'bar'或'barh'，而不是'hist' - 而不是
.groupby，使用.value_counts将简化代码。 - 用
ax.bar_label注释。请参阅 Adding value labels on a matplotlib bar chart 了解更多注释信息- 如果升级
matplotlib不是一个选项，请使用 Adding value labels on a matplotlib bar chart 中显示的旧注释方法。
- 如果升级
- 使用
pandas v1.2.4和matplotlib v3.4.2。
import pandas as pd
from pathlib import Path
# find the files
files = Path('data-society-global-terrorism-data/').glob('*.csv')
# create a single dataframe of the 4 csv files
gt = pd.concat([pd.read_csv(file, low_memory=False) for file in files]).reset_index(drop=True)
# display(gt.loc[:5, ['country_txt', 'eventid']]
country_txt eventid
0 Northern Ireland 199301010001
1 Egypt 199301010002
2 Sri Lanka 199301030001
3 Israel 199301030002
4 Israel 199301030003
5 Northern Ireland 199301030004
# groupby and get the top 20
# gtg = gt.groupby(["country_txt"])['eventid'].count().sort_values(ascending=False).head(20)
# optional: using value_counts is easier
gtg = gt.country_txt.value_counts().head(20)
# plot the figure as a bar plot, not a histogram
ax = gtg.plot(kind='barh', figsize=(7, 12))
# annotate the bars
ax.bar_label(ax.containers[-1], label_type='edge')
# bump out the x margin so the annotation doesn't stick out
ax.margins(x=0.1)
两列示例数据
data = {'country_txt': ['Colombia', 'Afghanistan', 'Colombia', 'Colombia', 'Pakistan', 'India', 'India', 'Colombia', 'India', 'Colombia', 'Colombia', 'Colombia', 'Colombia', 'Colombia', 'Colombia', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'India', 'Pakistan', 'India', 'India', 'India', 'India', 'India', 'Colombia', 'Colombia', 'Colombia', 'Afghanistan', 'India', 'Pakistan', 'Colombia', 'India', 'India', 'India', 'Pakistan', 'Pakistan', 'Pakistan', 'Colombia', 'India', 'India', 'India', 'Colombia', 'Colombia', 'India', 'Pakistan', 'India', 'India', 'India', 'India', 'Pakistan', 'Colombia', 'Colombia', 'Colombia', 'India', 'India', 'Colombia', 'Afghanistan', 'Iraq', 'India', 'India', 'India', 'India', 'Iraq', 'Iraq', 'Pakistan', 'Pakistan', 'Pakistan', 'Pakistan', 'Iraq', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Iraq', 'Pakistan', 'Iraq', 'Iraq', 'India', 'Pakistan', 'Pakistan', 'Afghanistan', 'India', 'Afghanistan', 'Iraq', 'Iraq', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Iraq', 'Pakistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Pakistan', 'Afghanistan', 'Afghanistan', 'Afghanistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Afghanistan', 'India', 'Pakistan', 'Iraq', 'Iraq', 'Iraq', 'Pakistan', 'Afghanistan', 'Pakistan', 'Pakistan', 'Pakistan', 'Iraq', 'Afghanistan', 'Afghanistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'India', 'Iraq', 'Iraq', 'Afghanistan', 'India', 'Pakistan', 'Afghanistan', 'Pakistan', 'Pakistan', 'Pakistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Pakistan', 'Pakistan', 'Pakistan', 'Afghanistan', 'Pakistan', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Afghanistan', 'Pakistan', 'Afghanistan', 'Afghanistan', 'Pakistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Afghanistan', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'Iraq', 'India', 'Afghanistan', 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gt = pd.DataFrame(data)