使用 Pandas 合并和可视化数据集

Question

For example, i have the following data frame in pandas cleaned and ready for merging.

DataFrame1 for average Income per year

Country | Year 1  | Year 2  | Year 3
  A     |   50    |   30    |   20
  B     |   70    |   20    |   90
  C     |   10    |   20    |   30

Dataframe2 for Fertility rate 

Country | Year 1 | Year 2 | Year 3
   A    |   1.5  |   2    |  2.5
   B    |   2    |   2    |   3
   C    |   1    |   1    |   4 

Basically im trying to show the relationship between DataFrame1 and DataFrame2 over the years on matplotlib. But i cant seem to merge them as they have the same headings as Years? In addition, i just cant seem to find a graph for me to compare these data on matplotlib when trying to use the X axis as the years.Any advice wold be great as im using the values above as the datasets are huge. Could it be that the data is too much?

Answer 1

Consider generating separate country plots with a secondary axis since you are tracking two metrics of different scales: Income and Fertility . For this setup, you will need to reshape your wide format to long with pandas.melt() . Then, iterate through the distinct countries to filter the data frames.

Data

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

df1 = pd.DataFrame({'Country': ['A', 'B', 'C'],
                    'Year 1': [50, 70, 10],
                    'Year 2': [30, 20, 20],
                    'Year 3': [20, 90, 30]})

df1 = df1.melt(id_vars='Country', value_name='Income', var_name='Year')

df2 = pd.DataFrame({'Country': ['A', 'B', 'C'],
                    'Year 1': [1.5, 2, 1],
                    'Year 2': [2.0, 2, 1],
                    'Year 3': [2.5, 3, 4]})

df2 = df2.melt(id_vars='Country', value_name='Fertility', var_name='Year')

Plot

for c in df1['Country'].unique():
    fig, ax1 = plt.subplots(figsize=(10,4))

    ax2 = ax1.twinx()
    df1[df1['Country']==c].plot(kind='line', x='Year', y='Income', ax=ax1, color='g', legend=False)
    df2[df2['Country']==c].plot(kind='line', x='Year', y='Fertility', ax=ax2, color='b', legend=False)

    plt.title('Country ' + c)
    ax1.set_xlabel('Years')
    ax1.set_ylabel('Average Income Per Year')
    ax2.set_ylabel('Fertility Rate')

    lines = ax1.get_lines() + ax2.get_lines()
    ax1.legend(lines, [l.get_label() for l in lines], loc='upper left')

    ax1.set_xticks(np.arange(3))
    ax1.set_xticklabels(df1["Year"].unique())

    plt.show()
    plt.clf()

plt.close()