日付ごとに売上を求めたり、と基礎的な集計はデータ分析の要です。
まずは、pandasのgroupbyの使い方の基礎を学びます。

元データ:adult.csv

In [25]: data1.head()
Out[25]:
   age  workclass  fnlwgt     education  educational_num      marital_status  \
0   25    Private  226802          11th                7       Never-married
1   38    Private   89814       HS-grad                9  Married-civ-spouse
2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse
3   44    Private  160323  Some-college               10  Married-civ-spouse
4   18          ?  103497  Some-college               10       Never-married

          occupation relationship   race  gender  capital_gain  capital_loss  \
0  Machine-op-inspct    Own-child  Black    Male             0             0
1    Farming-fishing      Husband  White    Male             0             0
2    Protective-serv      Husband  White    Male             0             0
3  Machine-op-inspct      Husband  Black    Male          7688             0
4                  ?    Own-child  White  Female             0             0

   hours_per_week native_country income
0              40  United-States  <=50K
1              50  United-States  <=50K
2              40  United-States   >50K
3              40  United-States   >50K
4              30  United-States  <=50K

データ読み込みを実施。

import pandas as pd
import numpy as np

# adult.csvの列データを読み込む
data1 = pd.read_csv("adult.csv")
# 列名のハイフンをアンダースコアに変換
data1.columns = data1.columns.str.replace("-", "_")

pandasのgroupbyでキー列ごとに、任意の列を集計する

# age列ごとに'educational_num'と'capital_gain'を合計
data1.groupby('age')[['educational_num', 'capital_gain']].sum()

結果はこちら↓

Out[27]:
     educational_num  capital_gain
age
17              3978         48341
18              7086         65653
19              9568        133518
20             10397         74501
21             10336        185647

色々な関数で集計を実施

特に、カウント、ユニークカウント、平均あたりは利用する機会が多いです。

# 平均
data1.groupby('age')[['educational_num', 'capital_gain']].mean()

# カウント
data1.groupby('age')[['workclass']].count()

# ユニークカウント
data1.groupby('age')[['workclass']].nunique()

# 中央値
data1.groupby('age')[['educational_num', 'capital_gain']].median()

# 最大
data1.groupby('age')[['educational_num', 'capital_gain']].max()

# 最小
data1.groupby('age')[['educational_num', 'capital_gain']].min()

# 分散
data1.groupby('age')[['educational_num', 'capital_gain']].var()

# 標準偏差
data1.groupby('age')[['educational_num', 'capital_gain']].std()