问题
I have a DataFrame in pandas with information about people location in time. It is about 300+ million rows.
Sample:
import pandas as pd
inp = [{'Name': 'John', 'Year':2018, 'Address':'Beverly hills'}, {'Name': 'John', 'Year':2018, 'Address':'Beverly hills'}, {'Name': 'John', 'Year':2019, 'Address':'Beverly hills'}, {'Name': 'John', 'Year':2019, 'Address':'Orange county'}, {'Name': 'John', 'Year':2019, 'Address':'New York'}, {'Name': 'Steve', 'Year':2018, 'Address':'Canada'}, {'Name': 'Steve', 'Year':2019, 'Address':'Canada'}, {'Name': 'Steve', 'Year':2019, 'Address':'Canada'}, {'Name': 'Steve', 'Year':2020, 'Address':'California'}, {'Name': 'Steve', 'Year':2020, 'Address':'Canada'}, {'Name': 'John', 'Year':2020, 'Address':'Canada'}, {'Name': 'John', 'Year':2021, 'Address':'Canada'}, {'Name': 'John', 'Year':2021, 'Address':'Beverly hills'}, {'Name': 'Steve', 'Year':2021, 'Address':'California'}, {'Name': 'Steve', 'Year':2022, 'Address':'California'}, {'Name': 'Steve', 'Year':2018, 'Address':'NewYork'}, {'Name': 'Steve', 'Year':2018, 'Address':'California'}, {'Name': 'Steve', 'Year':2022, 'Address':'NewYork'}]
df = pd.DataFrame(inp)
print (df)
Output:
Address Name Year
0 Beverly hills John 2018
1 Beverly hills John 2018
2 Beverly hills John 2019
3 Orange county John 2019
4 New York John 2019
5 Canada Steve 2018
6 Canada Steve 2019
7 Canada Steve 2019
8 California Steve 2020
9 Canada Steve 2020
10 Canada John 2020
11 Canada John 2021
12 Beverly hills John 2021
13 California Steve 2021
14 California Steve 2022
15 NewYork Steve 2018
16 California Steve 2018
17 NewYork Steve 2022
I want to calculate the total of changes between Addresses in a specific Year. Or in other words, how many times people moved from “Canada” to “California” in 2018.
Ideal Outputs:
1) Matrix as below for each year. Example: all address changes in the year 2019 (including 2018 to 2019).
+---------------+---------------+---------------+----------+------------+
| From\ To | Beverly hills | Orange county | New York | California |
+---------------+---------------+---------------+----------+------------+
| Beverly hills | 0 | 1 | 0 | 0 |
+---------------+---------------+---------------+----------+------------+
| Orange county | 0 | 0 | 1 | 0 |
+---------------+---------------+---------------+----------+------------+
| New York | 0 | 2 | 0 | 0 |
+---------------+---------------+---------------+----------+------------+
| California | 0 | 0 | 0 | 0 |
+---------------+---------------+---------------+----------+------------+
2) Address change for all years.
+---------------+---------------+------+------+------+
| Address 1 | Address 2 | 2018 | 2019 | 2020 |
+---------------+---------------+------+------+------+
| Beverly hills | Orange county | 0 | 1 | 0 |
+---------------+---------------+------+------+------+
| New York | Canada | 0 | 0 | 1 |
+---------------+---------------+------+------+------+
| Canada | New York | 1 | 0 | 0 |
+---------------+---------------+------+------+------+
| California | Canada | 0 | 1 | 2 |
+---------------+---------------+------+------+------+
My solution so far: Thanks to @QuangHoang I can capture the change of “Year” and change of “Address” with the following code:
groups = df.groupby('Name')
for col in ['Year', 'Address']:
df[f'cng-{col}'] = groups[col].shift().fillna(df[col]).ne(df[col]).astype(int)
groups[col].shift() shifts the corresponding column by 1 within each name. fillna(df[col] fills the first row in each (shifted) group with the original, indicating no change. Finally, ne(df[col]) compares the shifted values with the original values for changes.
Yields:
+----+---------------+-------+------+----------+-------------+
| ID | Address | Name | Year | cng-Year | cng-Address |
+----+---------------+-------+------+----------+-------------+
| 0 | Beverly hills | John | 2018 | 0 | 0 |
+----+---------------+-------+------+----------+-------------+
| 1 | Beverly hills | John | 2018 | 0 | 0 |
+----+---------------+-------+------+----------+-------------+
| 2 | Beverly hills | John | 2019 | 1 | 0 |
+----+---------------+-------+------+----------+-------------+
| 3 | Orange county | John | 2019 | 0 | 1 |
+----+---------------+-------+------+----------+-------------+
| 4 | New York | John | 2019 | 0 | 1 |
+----+---------------+-------+------+----------+-------------+
| 10 | Canada | John | 2020 | 1 | 1 |
+----+---------------+-------+------+----------+-------------+
| 11 | Canada | John | 2021 | 1 | 0 |
+----+---------------+-------+------+----------+-------------+
| 12 | Beverly hills | John | 2021 | 0 | 1 |
+----+---------------+-------+------+----------+-------------+
| 5 | Canada | Steve | 2018 | 0 | 0 |
+----+---------------+-------+------+----------+-------------+
| 15 | NewYork | Steve | 2018 | 1 | 1 |
+----+---------------+-------+------+----------+-------------+
| 16 | California | Steve | 2018 | 0 | 1 |
+----+---------------+-------+------+----------+-------------+
| 6 | Canada | Steve | 2019 | 1 | 0 |
+----+---------------+-------+------+----------+-------------+
| 7 | Canada | Steve | 2019 | 0 | 0 |
+----+---------------+-------+------+----------+-------------+
| 8 | California | Steve | 2020 | 1 | 1 |
+----+---------------+-------+------+----------+-------------+
| 9 | Canada | Steve | 2020 | 0 | 1 |
+----+---------------+-------+------+----------+-------------+
| 13 | California | Steve | 2021 | 1 | 1 |
+----+---------------+-------+------+----------+-------------+
| 14 | California | Steve | 2022 | 1 | 0 |
+----+---------------+-------+------+----------+-------------+
| 17 | NewYork | Steve | 2022 | 1 | 1 |
+----+---------------+-------+------+----------+-------------+
回答1:
If I understood the problem..
df.drop_duplicates().groupby(['Name','Year']).size().reset_index(name="changes")
With this output
Name Year changes
0 John 2018 1
1 John 2019 3
2 John 2020 1
3 John 2021 2
4 Steve 2018 3
5 Steve 2019 1
6 Steve 2020 2
7 Steve 2021 1
8 Steve 2022 2
来源：https://stackoverflow.com/questions/61216389/count-row-value-change-for-each-group-in-pandas-dataframe