2018年销售汇总表-pandas数据分析

import numpy as np
import pandas as pd
from sparklines import sparklines
df = pd.read_csv('2018年销售汇总表.csv',parse_dates = ['日期'],encoding="gbk")
df.head()# 查看前五条数据

	t	姓名	单品	数量	单价	金额	日期
0	740150	Barton LLC	B1-20000	39	86.69	3380.91	2018-01-01 07:21:00
1	714466	Trantow-Barrows	S2-77896	-1	63.16	-63.16	2018-01-01 10:00:00
2	218895	Kulas Inc	B1-69924	23	90.70	2086.10	2018-01-01 13:24:00
3	307599	Kassulke, Ondricka and Metz	S1-65481	41	21.05	863.05	2018-01-01 15:05:00
4	412290	Jerde-Hilpert	S2-34077	6	83.21	499.26	2018-01-01 23:26:00

df.info()#显示数据基础信息

RangeIndex: 1500 entries, 0 to 1499
Data columns (total 7 columns):
t     1500 non-null int64
姓名    1500 non-null object
单品    1500 non-null object
数量    1500 non-null int64
单价    1500 non-null float64
金额    1500 non-null float64
日期    1500 non-null datetime64[ns]
dtypes: datetime64[ns](1), float64(2), int64(2), object(2)
memory usage: 82.2+ KB

# 讲解：按《姓名》分组，聚合《金额》的均值和总和
# 用法：按客人姓名查看消费金额的平均值与总金额
df.groupby("姓名")["金额"].agg(["mean","sum"])

	mean	sum
姓名
Barton LLC	1334.615854	109438.50
Cronin, Oberbrunner and Spencer	1339.321642	89734.55
Frami, Hills and Schmidt	1438.466528	103569.59
Fritsch, Russel and Anderson	1385.366790	112214.71
Halvorson, Crona and Champlin	1206.971724	70004.36
Herman LLC	1336.532258	82865.00
Jerde-Hilpert	1265.072247	112591.43
Kassulke, Ondricka and Metz	1350.797969	86451.07
Keeling LLC	1363.977027	100934.30
Kiehn-Spinka	1260.870506	99608.77
Koepp Ltd	1264.152927	103660.54
Kuhn-Gusikowski	1247.866849	91094.28
Kulas Inc	1461.191064	137351.96
Pollich LLC	1196.536712	87347.18
Purdy-Kunde	1469.777547	77898.21
Sanford and Sons	1391.872958	98822.98
Stokes LLC	1271.332222	91535.92
Trantow-Barrows	1312.567872	123381.38
White-Trantow	1579.558023	135841.99
Will LLC	1411.318919	104437.60

# 讲解：在上一条命令中，并设置平均值与总金额的显示风格
# 用法：格式化数值，在前面添加美元符号，包括2位小数点
(
    df.groupby("姓名")["金额"]
    .agg(["mean","sum"])
    .style.format("${0:,.2f}")
)

	mean	sum
姓名
Barton LLC	$1,334.62	$109,438.50
Cronin, Oberbrunner and Spencer	$1,339.32	$89,734.55
Frami, Hills and Schmidt	$1,438.47	$103,569.59
Fritsch, Russel and Anderson	$1,385.37	$112,214.71
Halvorson, Crona and Champlin	$1,206.97	$70,004.36
Herman LLC	$1,336.53	$82,865.00
Jerde-Hilpert	$1,265.07	$112,591.43
Kassulke, Ondricka and Metz	$1,350.80	$86,451.07
Keeling LLC	$1,363.98	$100,934.30
Kiehn-Spinka	$1,260.87	$99,608.77
Koepp Ltd	$1,264.15	$103,660.54
Kuhn-Gusikowski	$1,247.87	$91,094.28
Kulas Inc	$1,461.19	$137,351.96
Pollich LLC	$1,196.54	$87,347.18
Purdy-Kunde	$1,469.78	$77,898.21
Sanford and Sons	$1,391.87	$98,822.98
Stokes LLC	$1,271.33	$91,535.92
Trantow-Barrows	$1,312.57	$123,381.38
White-Trantow	$1,579.56	$135,841.99
Will LLC	$1,411.32	$104,437.60

# 平均值与总金额显示为不带小数点
(
    df.groupby("姓名")["金额"]
    .agg(["mean","sum"])
    .style.format("${0:,.0f}")
)

	mean	sum
姓名
Barton LLC	$1,335	$109,438
Cronin, Oberbrunner and Spencer	$1,339	$89,735
Frami, Hills and Schmidt	$1,438	$103,570
Fritsch, Russel and Anderson	$1,385	$112,215
Halvorson, Crona and Champlin	$1,207	$70,004
Herman LLC	$1,337	$82,865
Jerde-Hilpert	$1,265	$112,591
Kassulke, Ondricka and Metz	$1,351	$86,451
Keeling LLC	$1,364	$100,934
Kiehn-Spinka	$1,261	$99,609
Koepp Ltd	$1,264	$103,661
Kuhn-Gusikowski	$1,248	$91,094
Kulas Inc	$1,461	$137,352
Pollich LLC	$1,197	$87,347
Purdy-Kunde	$1,470	$77,898
Sanford and Sons	$1,392	$98,823
Stokes LLC	$1,271	$91,536
Trantow-Barrows	$1,313	$123,381
White-Trantow	$1,580	$135,842
Will LLC	$1,411	$104,438

# 按月分析销售金额，并显示每月销售额占年度销售总额比例
by=[pd.Grouper(key='日期', freq='M')]#自定义字段的格式
monthly_sales=df.groupby(by)["金额"].agg(["sum"]).reset_index()
monthly_sales["月占比"]=monthly_sales["sum"]/df["金额"].sum()

format_dict = {'sum': '${0:,.0f}', '日期': '{:%Y-%m-%d}', '月占比': '{:.4%}'}
# 1.使用普通格式字典控制每列的格式
(
    monthly_sales
    .style
    .format(format_dict)
    .hide_index()
)

日期	sum	月占比
2018-01-31	$185,362	9.1818%
2018-02-28	$146,212	7.2426%
2018-03-31	$203,921	10.1012%
2018-04-30	$174,574	8.6475%
2018-05-31	$165,419	8.1940%
2018-06-30	$174,089	8.6235%
2018-07-31	$191,662	9.4939%
2018-08-31	$153,779	7.6174%
2018-09-30	$168,443	8.3438%
2018-10-31	$171,495	8.4950%
2018-11-30	$119,961	5.9423%
2018-12-31	$163,867	8.1171%

# 按月分析销售金额，并显示每月销售额占年度销售总额比例
by=[pd.Grouper(key='日期', freq='M')]#自定义字段的格式
monthly_sales=df.groupby(by)["金额"].agg(["sum"]).reset_index()
monthly_sales["月占比"]=monthly_sales["sum"]/df["金额"].sum()

# 2.使用条件格式字典控制每列的格式
format_dict = {'sum': '${0:,.0f}', '日期': '{:%Y-%m-%d}', '月占比': '{:.4%}'}
(
    monthly_sales
    .style
    .format(format_dict)
    .hide_index()
    .highlight_max(color="lightgreen")
    .highlight_min(color="red")
)

日期	sum	月占比
2018-01-31	$185,362	9.1818%
2018-02-28	$146,212	7.2426%
2018-03-31	$203,921	10.1012%
2018-04-30	$174,574	8.6475%
2018-05-31	$165,419	8.1940%
2018-06-30	$174,089	8.6235%
2018-07-31	$191,662	9.4939%
2018-08-31	$153,779	7.6174%
2018-09-30	$168,443	8.3438%
2018-10-31	$171,495	8.4950%
2018-11-30	$119,961	5.9423%
2018-12-31	$163,867	8.1171%

# 按月分析销售金额，并显示每月销售额占年度销售总额比例
by=[pd.Grouper(key='日期', freq='M')]#自定义字段的格式
monthly_sales=df.groupby(by)["金额"].agg(["sum"]).reset_index()
monthly_sales["月占比"]=monthly_sales["sum"]/df["金额"].sum()

# 3.使用渐变色格式字典控制每列的格式
format_dict = {'sum': '${0:,.0f}', '日期': '{:%Y-%m-%d}', '月占比': '{:.4%}'}
(
    monthly_sales
    .style
    .format(format_dict)
    .background_gradient(subset=["sum"],cmap="PRGn")
)

	日期	sum	月占比
0	2018-01-31	$185,362	9.1818%
1	2018-02-28	$146,212	7.2426%
2	2018-03-31	$203,921	10.1012%
3	2018-04-30	$174,574	8.6475%
4	2018-05-31	$165,419	8.1940%
5	2018-06-30	$174,089	8.6235%
6	2018-07-31	$191,662	9.4939%
7	2018-08-31	$153,779	7.6174%
8	2018-09-30	$168,443	8.3438%
9	2018-10-31	$171,495	8.4950%
10	2018-11-30	$119,961	5.9423%
11	2018-12-31	$163,867	8.1171%

# 按月分析销售金额，并显示每月销售额占年度销售总额比例
by=[pd.Grouper(key='日期', freq='M')]#自定义字段的格式
monthly_sales=df.groupby(by)["金额"].agg(["sum"]).reset_index()
monthly_sales["月占比"]=monthly_sales["sum"]/df["金额"].sum()

# 4.使用单元格柱形格式字典控制每列的格式
format_dict = {'sum': '${0:,.0f}', '日期': '{:%Y-%m-%d}', '月占比': '{:.4%}'}
(
    monthly_sales
    .style
    .format(format_dict)
    .hide_index()
    .bar(color="#FFA07A",vmin=100_000,subset=["sum"],align="zero")
    .bar(color='lightblue', vmin=0, subset=['月占比'], align='zero')
    .set_caption("2018年销售一览表")
)

2018年销售一览表

日期	sum	月占比
2018-01-31	$185,362	9.1818%
2018-02-28	$146,212	7.2426%
2018-03-31	$203,921	10.1012%
2018-04-30	$174,574	8.6475%
2018-05-31	$165,419	8.1940%
2018-06-30	$174,089	8.6235%
2018-07-31	$191,662	9.4939%
2018-08-31	$153,779	7.6174%
2018-09-30	$168,443	8.3438%
2018-10-31	$171,495	8.4950%
2018-11-30	$119,961	5.9423%
2018-12-31	$163,867	8.1171%

def sparkline_str(x):
    bins=np.histogram(x)[0]#直方图
    s1="".join(sparklines(bins))
    return s1
sparkline_str.__name__ = "走势图"
df.groupby("姓名")["数量","金额"].agg(["mean",sparkline_str])

	数量		金额
	mean	走势图	mean	走势图
姓名
Barton LLC	24.890244	▄▄▃▂▃▆▄█▁▄	1334.615854	█▄▃▆▄▄▁▁▁▁
Cronin, Oberbrunner and Spencer	24.970149	█▄▁▄▄▇▅▁▄▄	1339.321642	█▅▅▃▃▃▂▂▁▁
Frami, Hills and Schmidt	26.430556	▄▄▁▂▇█▂▂▅▅	1438.466528	█▅▄▇▅▃▄▁▁▁
Fritsch, Russel and Anderson	26.074074	▁▄▇▃▂▂█▃▄▄	1385.366790	▇█▃▄▂▂▁▂▁▁
Halvorson, Crona and Champlin	22.137931	▇▆▆▇█▁▄▂▄▃	1206.971724	██▆▅▁▃▂▂▂▂
Herman LLC	24.806452	▄▃▅▁▆▄▂▆▃█	1336.532258	█▅▇▄▅▄▁▃▂▂
Jerde-Hilpert	22.460674	▄▄█▁▂▅▃▂▄▃	1265.072247	█▄▅▂▁▂▃▂▂▁
Kassulke, Ondricka and Metz	25.734375	▂▂▁▁▂▂▁▅▄█	1350.797969	█▆▆▄▄▃▂▁▁▂
Keeling LLC	24.405405	▁▄▇▃▅█▃▄▃▆	1363.977027	▅█▆▃▄▂▂▁▁▁
Kiehn-Spinka	22.227848	▃▂█▂▃▅▄▁▄▁	1260.870506	█▇▄▃▃▂▁▂▁▁
Koepp Ltd	21.829268	▅▇█▆▄▇▅▁▅▇	1264.152927	█▇▅▂▄▂▂▂▁▁
Kuhn-Gusikowski	22.808219	▂▄█▄▃▁█▄▂▄	1247.866849	▆█▄▃▃▃▃▁▁▁
Kulas Inc	24.095745	▇▃▇▂▇▁▅▆█▆	1461.191064	█▅█▂▄▄▄▂▁▁
Pollich LLC	23.383562	█▂▅▂▃▆▁▂▄▇	1196.536712	█▆▆▃▃▃▂▂▁▁
Purdy-Kunde	27.358491	▄▅█▁▄▇▅█▇█	1469.777547	█▅▄▃▄▃▃▂▁▂
Sanford and Sons	24.000000	▂▂▂▆▁█▂▂▁▅	1391.872958	▅▇█▃▃▄▁▃▁▂
Stokes LLC	24.527778	▄▄▄▁▃▂▄█▄▄	1271.332222	█▇▆▆▄▂▂▃▁▂
Trantow-Barrows	24.159574	▅▄▅▂▃▃▄▄▁█	1312.567872	█▇▅▆▃▂▂▁▁▁
White-Trantow	26.255814	▃▂▆▆▃██▁██	1579.558023	▄█▆▅▄▃▄▂▁▃
Will LLC	24.702703	▂█▁▄▄▄▂▅▅▄	1411.318919	▆█▄▅▄▂▁▂▁▂