前言
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柱狀圖
柱狀圖有效地傳達了專案的排名順序，但是，在圖表上方添加度量標準的值，用戶可以從圖表本身獲取精確資訊，
# Prepare Data df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/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=(16,10), facecolor='white', dpi= 80) ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', 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':22}) ax.set(ylabel='Miles Per Gallon', ylim=(0, 30)) plt.xticks(df.index, df.manufacturer.str.upper(), rotation=60, horizontalalignment='right', fontsize=12) # Add patches to color the X axis labels 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()
棒棒糖圖
棒棒糖圖表以一種視覺上令人愉悅的方式提供與有序條形圖類似的目的，
# Prepare Data df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/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=(16,10), dpi= 80) ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', alpha=0.7, linewidth=2) ax.scatter(x=df.index, y=df.cty, s=75, color='firebrick', alpha=0.7) # Title, Label, Ticks and Ylim ax.set_title('Lollipop Chart for Highway Mileage', fontdict={'size':22}) ax.set_ylabel('Miles Per Gallon') ax.set_xticks(df.index) ax.set_xticklabels(df.manufacturer.str.upper(), rotation=60, fontdict={'horizontalalignment': 'right', 'size':12}) ax.set_ylim(0, 30) # Annotate for row in df.itertuples(): ax.text(row.Index, row.cty+.5, s=round(row.cty, 2), horizontalalignment= 'center', verticalalignment='bottom', fontsize=14) plt.show()
連續變數的直方圖
直方圖顯示給定變數的頻率分布，下面的表示基于分類變數對頻率條進行分組，從而更好地了解連續變數和串聯變數，
# Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare data x_var = 'displ' groupby_var = 'class' df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var) vals = [df[x_var].values.tolist() for i, df in df_agg] # Draw plt.figure(figsize=(16,9), dpi= 80) colors = [plt.cm.Spectral(i/float(len(vals)-1)) for i in range(len(vals))] n, bins, patches = plt.hist(vals, 30, stacked=True, density=False, color=colors[:len(vals)]) # Decoration plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])}) plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22) plt.xlabel(x_var) plt.ylabel("Frequency") plt.ylim(0, 25) plt.xticks(ticks=bins[::3], labels=[round(b,1) for b in bins[::3]]) plt.show()
分類變數的直方圖
分類變數的直方圖顯示該變數的頻率分布，通過對條形圖進行著色，您可以將分布與表示顏色的另一個分類變數相關聯，
# Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare data x_var = 'manufacturer' groupby_var = 'class' df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var) vals = [df[x_var].values.tolist() for i, df in df_agg] # Draw plt.figure(figsize=(16,9), dpi= 80) colors = [plt.cm.Spectral(i/float(len(vals)-1)) for i in range(len(vals))] n, bins, patches = plt.hist(vals, df[x_var].unique().__len__(), stacked=True, density=False, color=colors[:len(vals)]) # Decoration plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])}) plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22) plt.xlabel(x_var) plt.ylabel("Frequency") plt.ylim(0, 40) plt.xticks(ticks=bins, labels=np.unique(df[x_var]).tolist(), rotation=90, horizontalalignment='left') plt.show()
散點圖
Scatteplot是用于研究兩個變數之間關系的經典和基本圖，如果資料中有多個組，則可能需要以不同顏色可視化每個組，在Matplotlib，你可以方便地使用，
# Import dataset midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv") # Prepare Data # Create as many colors as there are unique midwest['category'] categories = np.unique(midwest['category']) colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))] # Draw Plot for Each Category plt.figure(figsize=(16, 10), dpi= 80, facecolor='w', edgecolor='k') for i, category in enumerate(categories): plt.scatter('area', 'poptotal', data=midwest.loc[midwest.category==category, :], s=20, c=colors[i], label=str(category)) # Decorations plt.gca().set(xlim=(0.0, 0.1), ylim=(0, 90000), xlabel='Area', ylabel='Population') plt.xticks(fontsize=12); plt.yticks(fontsize=12) plt.title("Scatterplot of Midwest Area vs Population", fontsize=22) plt.legend(fontsize=12) plt.show()
樹狀圖
樹狀圖根據給定的距離度量將相似的點組合在一起，并根據該點的相似性將它們組織成樹狀鏈接，
import scipy.cluster.hierarchy as shc # Import Data df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/USArrests.csv') # Plot plt.figure(figsize=(16, 10), dpi= 80) plt.title("USArrests Dendograms", fontsize=22) dend = shc.dendrogram(shc.linkage(df[['Murder', 'Assault', 'UrbanPop', 'Rape']], method='ward'), labels=df.State.values, color_threshold=100) plt.xticks(fontsize=12) plt.show()
人口金字塔
人口金字塔可用于顯示按體積排序的組的分布，或者，它也可以用來顯示人口的逐步過濾，因為它在下面用于顯示有多少人通過營銷渠道的每個階段，
# Read data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv") # Draw Plot plt.figure(figsize=(13,10), dpi= 80) group_col = 'Gender' order_of_bars = df.Stage.unique()[::-1] colors = [plt.cm.Spectral(i/float(len(df[group_col].unique())-1)) for i in range(len(df[group_col].unique()))] for c, group in zip(colors, df[group_col].unique()): sns.barplot(x='Users', y='Stage', data=https://www.cnblogs.com/hhh188764/p/df.loc[df[group_col]==group, :], order=order_of_bars, color=c, label=group) # Decorations plt.xlabel("$Users$") plt.ylabel("Stage of Purchase") plt.yticks(fontsize=12) plt.title("Population Pyramid of the Marketing Funnel", fontsize=22) plt.legend() plt.show()
餅圖
餅圖是顯示組組成的經典方法，但是，如今一般不建議使用它，因為餡餅部分的面積有時可能會引起誤解，因此，如果要使用餅圖，強烈建議明確寫下餅圖各部分的百分比或數字，
# Import df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare Data df = df_raw.groupby('class').size() # Make the plot with pandas df.plot(kind='pie', subplots=True, figsize=(8, 8), dpi= 80) plt.title("Pie Chart of Vehicle Class - Bad") plt.ylabel("") plt.show()
時間序列圖
時間序列圖用于可視化給定指標如何隨時間變化，在這里，您可以了解1949年至1969年之間的航空客運流量如何變化，
# Import Data df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv') # Draw Plot plt.figure(figsize=(16,10), dpi= 80) plt.plot('date', 'traffic', data=https://www.cnblogs.com/hhh188764/p/df, color='tab:red') # Decoration plt.ylim(50, 750) xtick_location = df.index.tolist()[::12] xtick_labels = [x[-4:] for x in df.date.tolist()[::12]] plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=0, fontsize=12, horizontalalignment='center', alpha=.7) plt.yticks(fontsize=12, alpha=.7) plt.title("Air Passengers Traffic (1949 - 1969)", fontsize=22) plt.grid(axis='both', alpha=.3) # Remove borders plt.gca().spines["top"].set_alpha(0.0) plt.gca().spines["bottom"].set_alpha(0.3) plt.gca().spines["right"].set_alpha(0.0) plt.gca().spines["left"].set_alpha(0.3) plt.show()
區域圖未堆疊
未堆積的面積圖用于可視化兩個或多個系列相對于彼此的進度（漲跌），在下面的圖表中，您可以清楚地看到隨著失業時間的中位數增加，個人儲蓄率如何下降，未堆積面積圖很好地顯示了這種現象，
顯示代碼
# Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/economics.csv") # Prepare Data x = df['date'].values.tolist() y1 = df['psavert'].values.tolist() y2 = df['uempmed'].values.tolist() mycolors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange', 'tab:brown', 'tab:grey', 'tab:pink', 'tab:olive'] columns = ['psavert', 'uempmed'] # Draw Plot fig, ax = plt.subplots(1, 1, figsize=(16,9), dpi= 80) ax.fill_between(x, y1=y1, y2=0, label=columns[1], alpha=0.5, color=mycolors[1], linewidth=2) ax.fill_between(x, y1=y2, y2=0, label=columns[0], alpha=0.5, color=mycolors[0], linewidth=2) # Decorations ax.set_title('Personal Savings Rate vs Median Duration of Unemployment', fontsize=18) ax.set(ylim=[0, 30]) ax.legend(loc='best', fontsize=12) plt.xticks(x[::50], fontsize=10, horizontalalignment='center') plt.yticks(np.arange(2.5, 30.0, 2.5), fontsize=10) plt.xlim(-10, x[-1]) # Draw Tick lines for y in np.arange(2.5, 30.0, 2.5): plt.hlines(y, xmin=0, xmax=len(x), colors='black', alpha=0.3, linestyles="--", lw=0.5) # Lighten borders plt.gca().spines["top"].set_alpha(0) plt.gca().spines["bottom"].set_alpha(.3) plt.gca().spines["right"].set_alpha(0) plt.gca().spines["left"].set_alpha(.3) plt.show()
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標籤：Python
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