❝Cet article partage "5 diagrammes de relations de classement (classement) les plus couramment utilisés" .
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table des matières
Troisièmement, le diagramme de relation de classement (classement)
17. Diagramme à barres ordonné
Troisièmement, le diagramme de relation de classement (classement)
17. Diagramme à barres ordonné
# Prepare Data
df_raw = pd.read_csv("./datasets/mpg_ggplot2.csv")
df = df_raw[['cty',
'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())
df.sort_values('cty', inplace=True)
df.reset_index(inplace=True)
# Draw plot
import matplotlib.patches as patches
fig, ax = plt.subplots(figsize=(10, 8), facecolor='white', dpi=80)
ax.vlines(x=df.index,
ymin=0,
ymax=df.cty,
color='#dc2624',
alpha=0.7,
linewidth=20)
# Annotate Text
for i, cty in enumerate(df.cty):
ax.text(i, cty + 0.5, round(cty, 1), horizontalalignment='center')
# Title, Label, Ticks and Ylim
ax.set_title('Bar Chart for Highway Mileage', fontdict={'size': 12})
plt.xticks(df.index,
df.manufacturer.str.upper(),
rotation=60,
horizontalalignment='right',
fontsize=10)
plt.yticks(fontsize=12)
plt.ylabel('Miles Per Gallon', fontsize=12)
plt.ylim = (0, 30)
# 添加底纹
p1 = patches.Rectangle((.57, -0.005),
width=.33,
height=.13,
alpha=.1,
facecolor='green',
transform=fig.transFigure)
p2 = patches.Rectangle((.124, -0.005),
width=.446,
height=.13,
alpha=.1,
facecolor='red',
transform=fig.transFigure)
fig.add_artist(p1)
fig.add_artist(p2)
plt.show()
Plus d'introduction de graphique à barres:
18. Graphique Lollipop
Remplacez simplement les piliers ci-dessus par des bâtons, et l'effet est le même ~~
# 棒棒糖图(Lollipop Chart)
# Prepare Data
df_raw = pd.read_csv("./datasets/mpg_ggplot2.csv")
df = df_raw[['cty',
'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())
df.sort_values('cty', inplace=True)
df.reset_index(inplace=True)
# Draw plot
fig, ax = plt.subplots(figsize=(10, 8), dpi=80)
ax.vlines(x=df.index,
ymin=0,
ymax=df.cty,
color='#dc2624',
alpha=0.7,
linewidth=4)
ax.scatter(x=df.index, y=df.cty, s=85, color='#dc2624', alpha=0.7)
# Title, Label, Ticks and Ylim
ax.set_title('Lollipop Chart for Highway Mileage', fontdict={'size': 12})
plt.ylabel('Miles Per Gallon', fontsize=12)
ax.set_xticks(df.index)
ax.set_xticklabels(df.manufacturer.str.upper(),
rotation=60,
fontdict={
'horizontalalignment': 'right',
'size': 11
})
ax.set_ylim(0, 30)
plt.yticks(fontsize=12)
# Annotate
for row in df.itertuples():
ax.text(row.Index,
row.cty + .5,
s=round(row.cty, 2),
horizontalalignment='center',
verticalalignment='bottom',
fontsize=12)
plt.show()
19. Tracé de points
Retirez simplement les bâtons ci-dessus et placez-les horizontalement, l'effet est le même ~~, montrant le classement de chaque indicateur dans la direction horizontale.
# Prepare Data
df_raw = pd.read_csv("./datasets/mpg_ggplot2.csv")
df = df_raw[['cty',
'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())
df.sort_values('cty', inplace=True)
df.reset_index(inplace=True)
# Draw plot
fig, ax = plt.subplots(figsize=(10, 8), dpi=80)
ax.hlines(y=df.index,
xmin=11,
xmax=26,
color='gray',
alpha=0.7,
linewidth=1,
linestyles='dashdot')
ax.scatter(y=df.index, x=df.cty, s=75, color='#dc2624', alpha=0.7)
# Title, Label, Ticks and Ylim
ax.set_title('Dot Plot for Highway Mileage', fontdict={'size': 12})
plt.xlabel('Miles Per Gallon', fontsize=12)
ax.set_yticks(df.index)
ax.set_yticklabels(df.manufacturer.str.title(),
fontdict={
'horizontalalignment': 'right',
'fontsize': 12
})
plt.xticks(fontsize=12)
ax.set_xlim(10, 27)
plt.show()
20. Carte des pentes
Il est bon de comparer la situation de plusieurs projets sur deux périodes différentes.
#comparing the ‘Before’ and ‘After’ positions of a given person/item
import matplotlib.lines as mlines
# Import Data
df = pd.read_csv("./datasets/gdppercap.csv")
left_label = [
str(c) + ', ' + str(round(y)) for c, y in zip(df.continent, df['1952'])
]
right_label = [
str(c) + ', ' + str(round(y)) for c, y in zip(df.continent, df['1957'])
]
klass = [
'red' if (y1 - y2) < 0 else 'green'
for y1, y2 in zip(df['1952'], df['1957'])
]
# draw line
# https://stackoverflow.com/questions/36470343/how-to-draw-a-line-with-matplotlib/36479941
def newline(p1, p2, color='black'):
ax = plt.gca()
l = mlines.Line2D([p1[0], p2[0]], [p1[1], p2[1]],
color='red' if p1[1] - p2[1] > 0 else 'green',
marker='o',
markersize=6)
ax.add_line(l)
return l
fig, ax = plt.subplots(1, 1, figsize=(10, 8), dpi=80)
# Vertical Lines
ax.vlines(x=1,
ymin=500,
ymax=13000,
color='black',
alpha=0.7,
linewidth=1,
linestyles='dotted')
ax.vlines(x=3,
ymin=500,
ymax=13000,
color='black',
alpha=0.7,
linewidth=1,
linestyles='dotted')
# Points
ax.scatter(y=df['1952'],
x=np.repeat(1, df.shape[0]),
s=10,
color='black',
alpha=0.7)
ax.scatter(y=df['1957'],
x=np.repeat(3, df.shape[0]),
s=10,
color='black',
alpha=0.7)
# Line Segmentsand Annotation
for p1, p2, c in zip(df['1952'], df['1957'], df['continent']):
newline([1, p1], [3, p2])
ax.text(1 - 0.05,
p1,
c + ', ' + str(round(p1)),
horizontalalignment='right',
verticalalignment='center',
fontdict={'size': 14})
ax.text(3 + 0.05,
p2,
c + ', ' + str(round(p2)),
horizontalalignment='left',
verticalalignment='center',
fontdict={'size': 14})
# 'Before' and 'After' Annotations
ax.text(1 - 0.05,
13000,
'BEFORE',
horizontalalignment='right',
verticalalignment='center',
fontdict={
'size': 15,
'weight': 700
})
ax.text(3 + 0.05,
13000,
'AFTER',
horizontalalignment='left',
verticalalignment='center',
fontdict={
'size': 15,
'weight': 700
})
# Decoration
ax.set_title("Slopechart: Comparing GDP Per Capita between 1952 vs 1957",
fontdict={'size': 18})
ax.set(xlim=(0, 4), ylim=(0, 14000), ylabel='Mean GDP Per Capita')
plt.ylabel('Mean GDP Per Capita', fontsize=15)
ax.set_xticks([1, 3])
ax.set_xticklabels(["1952", "1957"], fontdict={'size': 15, 'weight': 700})
plt.yticks(np.arange(500, 13000, 2000), fontsize=12)
# Lighten borders
plt.gca().spines["top"].set_alpha(.0)
plt.gca().spines["bottom"].set_alpha(.0)
plt.gca().spines["right"].set_alpha(.0)
plt.gca().spines["left"].set_alpha(.0)
plt.show()
21. Diagramme d'haltères
C'est un bon moyen de comparer la situation de plusieurs projets sur deux périodes différentes et, plus important encore, il affiche également les informations de tri de différents projets.
#显示排序和处理前后值范围
import matplotlib.lines as mlines
# Import Data
df = pd.read_csv("./datasets/health.csv")
df.sort_values('pct_2014', inplace=True)
df.reset_index(inplace=True)
# Func to draw line segment
def newline(p1, p2, color='black'):
ax = plt.gca()
l = mlines.Line2D([p1[0], p2[0]], [p1[1], p2[1]], color='#d5695d')
ax.add_line(l)
return l
# Figure and Axes
fig, ax = plt.subplots(1, 1, figsize=(10, 8), facecolor='#f8f2e4', dpi=80)
# Vertical Lines
ax.vlines(x=.05,
ymin=0,
ymax=26,
color='black',
alpha=1,
linewidth=1,
linestyles='dotted')
ax.vlines(x=.10,
ymin=0,
ymax=26,
color='black',
alpha=1,
linewidth=1,
linestyles='dotted')
ax.vlines(x=.15,
ymin=0,
ymax=26,
color='black',
alpha=1,
linewidth=1,
linestyles='dotted')
ax.vlines(x=.20,
ymin=0,
ymax=26,
color='black',
alpha=1,
linewidth=1,
linestyles='dotted')
# Points
ax.scatter(y=df['index'], x=df['pct_2013'], s=50, color='#dc2624')
ax.scatter(y=df['index'], x=df['pct_2014'], s=50, color='#e87a59')
# Line Segments
for i, p1, p2 in zip(df['index'], df['pct_2013'], df['pct_2014']):
newline([p1, i], [p2, i])
# Decoration
ax.set_facecolor('#f8f2e4')
ax.set_title("Dumbell Chart: Pct Change - 2013 vs 2014", fontdict={'size': 18})
ax.set(xlim=(0, .25), ylim=(-1, 27), ylabel='Mean GDP Per Capita')
plt.ylabel('Mean GDP Per Capita', fontsize=15)
plt.yticks(fontsize=15)
ax.set_xticks([.05, .1, .15, .20])
ax.set_xticklabels(['5%', '15%', '20%', '25%'], fontdict={'size': 15})
plt.show()