将两个熊猫数据框组合在一起Python [重复]答案

【问题标题】：Combining two pandas dataframes together Python [duplicate]将两个熊猫数据框组合在一起Python [重复]
【发布时间】：2021-11-08 05:14:42
【问题描述】：

下面的代码计算与日期 month_changes 和 month_changes_2 相关的 vals 和 vals_2 值的 list_val。该代码通过分隔年份间隔来计算 mean','median' or 'max','min'。我想对将两个输出图和 graph_2 组合在一起并返回下面的预期输出的代码进行一些操作，我将如何做到这一点？下面的代码来自这个问题的答案：link

代码：

import numpy as np 
import pandas as pd 

month_changes = np.array(["2018-04-01 00:00:00", "2018-05-01 00:00:00", "2019-03-01 00:00:00", "2019-04-01 00:00:00","2019-08-01 00:00:00", "2019-11-01 00:00:00", "2019-12-01 00:00:00","2021-01-01 00:00:00"]) 
vals = np.array([10, 23, 45, 4,5,12,4,-6])

month_changes_2 = np.array(["2018-04-06 00:00:00", "2018-05-13 00:00:00", "2018-03-01 00:00:00", "2019-02-01 00:00:00","2019-03-12 00:00:00", "2019-12-01 00:00:00", "2019-12-22 00:00:00","2020-04-01 00:00:00","2021-01-01 00:00:00"]) 
vals_2 = np.array([140, 213, 15, 4,53,1,42,-63,120])

list_val = ['mean', 'median', 'max', 'min']
def yearly_intervals(mc, vs, start_year, end_year,series_val):
    print(series_val)
    data = pd.DataFrame({
        "Date": pd.to_datetime(mc),  # Convert to_datetime immediately
        "Averages": vs
    })
    out = (
        data.groupby(data["Date"].dt.year)["Averages"]  # Access Series
            .agg(list_val[series_val[0]:series_val[-1]])
            .rename(columns=lambda x: 'Average' if x == 'mean' else x.title())
    )
    # If start_year
    if start_year is not None:
        # Reindex to ensure index contains all years in range
        out = out.reindex(range(
            start_year,
            # Use last year (maximum value) from index or user defined arg
            (end_year if end_year is not None else out.index.max()) + 1
        ), fill_value=0)
    return out

graph= yearly_intervals(month_changes, vals, start_year=2016, end_year=2021,series_val=[0,2])
graph_2= yearly_intervals(month_changes_2, vals_2, start_year=2016, end_year=2021,series_val = [2,4])

输出：

      Average  Median
Date                 
2016      0.0     0.0
2017      0.0     0.0
2018     16.5    16.5
2019     14.0     5.0
2020      0.0     0.0
2021     -6.0    -6.0

      Max  Min
Date          
2016    0    0
2017    0    0
2018  213   15
2019   53    1
2020  -63  -63
2021  120  120

预期输出

      Average  Median  Max  Min
Date                 
2016      0.0     0.0   0    0
2017      0.0     0.0   0    0
2018     16.5    16.5  213   15
2019     14.0     5.0   53    1
2020      0.0     0.0  -63  -63
2021     -6.0    -6.0  120  120

【问题讨论】：

df1.join(df2)?

标签： python arrays pandas numpy datetime

【解决方案1】：

这样的？


import pandas as pd
df1 = pd.DataFrame({
    'Average' : [0.0, 0.0, 16.5],
    'Median' : [0.0, 0.0, 16.5]
}, index=[2016, 2017, 2018])

df2 = pd.DataFrame({
    'Max' : [0, 0, 213],
    'Min' : [0, 0, 15]
}, index= [2016, 2017, 2018])


print(df1)
print(df2)

df = pd.concat([df1, df2], axis=1)

print(df)

【讨论】：

【解决方案2】：

我假设您已经创建并处理了两个数据框 graph 和 graph_2。

试试这个

combined_df = pd.concat([graph, graph_2], axis=1)
print(combined_df)

它会输出：

      Average  Median  Max  Min
Date
2016      0.0     0.0    0    0
2017      0.0     0.0    0    0
2018     16.5    16.5  213   15
2019     14.0     5.0   53    1
2020      0.0     0.0  -63  -63
2021     -6.0    -6.0  120  120

【讨论】：

【解决方案3】：

只需使用您现有的工作并运行graph.join(graph_2)：

import numpy as np 
import pandas as pd 

month_changes = np.array(["2018-04-01 00:00:00", "2018-05-01 00:00:00", "2019-03-01 00:00:00", "2019-04-01 00:00:00","2019-08-01 00:00:00", "2019-11-01 00:00:00", "2019-12-01 00:00:00","2021-01-01 00:00:00"]) 
vals = np.array([10, 23, 45, 4,5,12,4,-6])

month_changes_2 = np.array(["2018-04-06 00:00:00", "2018-05-13 00:00:00", "2018-03-01 00:00:00", "2019-02-01 00:00:00","2019-03-12 00:00:00", "2019-12-01 00:00:00", "2019-12-22 00:00:00","2020-04-01 00:00:00","2021-01-01 00:00:00"]) 
vals_2 = np.array([140, 213, 15, 4,53,1,42,-63,120])

list_val = ['mean', 'median', 'max', 'min']
def yearly_intervals(mc, vs, start_year, end_year,series_val):
    print(series_val)
    data = pd.DataFrame({
        "Date": pd.to_datetime(mc),  # Convert to_datetime immediately
        "Averages": vs
    })
    out = (
        data.groupby(data["Date"].dt.year)["Averages"]  # Access Series
            .agg(list_val[series_val[0]:series_val[-1]])
            .rename(columns=lambda x: 'Average' if x == 'mean' else x.title())
    )
    # If start_year
    if start_year is not None:
        # Reindex to ensure index contains all years in range
        out = out.reindex(range(
            start_year,
            # Use last year (maximum value) from index or user defined arg
            (end_year if end_year is not None else out.index.max()) + 1
        ), fill_value=0)
    return out

graph= yearly_intervals(month_changes, vals, start_year=2016, end_year=2021,series_val=[0,2])
graph_2= yearly_intervals(month_changes_2, vals_2, start_year=2016, end_year=2021,series_val = [2,4])

print(graph.join(graph_2))

打印出来的

[0, 2]
[2, 4]

      Average  Median  Max  Min
Date                           
2016      0.0     0.0    0    0
2017      0.0     0.0    0    0
2018     16.5    16.5  213   15
2019     14.0     5.0   53    1
2020      0.0     0.0  -63  -63
2021     -6.0    -6.0  120  120

【讨论】：