❝Este artículo comparte "Los 5 diagramas de relación de clasificación (Ranking) más utilizados" .
❞
Tabla de contenido
En tercer lugar, el diagrama de relación de ranking (Ranking)
17. Gráfico de barras ordenado
En tercer lugar, el diagrama de relación de ranking (Ranking)
17. Gráfico de barras ordenado
# 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()
Más introducción al gráfico de barras:
18. Gráfico de piruletas
Simplemente reemplace los pilares de arriba con palos, y el efecto es el mismo ~~
# 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. Gráfico de puntos
Simplemente retire los palos anteriores y colóquelos horizontalmente, el efecto es el mismo ~~, mostrando la clasificación de cada indicador en la dirección horizontal.
# 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. Gráfico de pendientes
Es bueno comparar la situación de múltiples proyectos en dos períodos diferentes.
#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. Gráfico con mancuernas
Es una buena forma de comparar la situación de varios proyectos en dos períodos diferentes y, lo que es más importante, también muestra la información de clasificación de diferentes proyectos.
#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()