Panda cukup bagus dalam menangani data. Berikut ini satu contoh cara menggunakannya:
import pandas as pd
# Read the CSV into a pandas data frame (df)
# With a df you can do many things
# most important: visualize data with Seaborn
df = pd.read_csv('filename.csv', delimiter=',')
# Or export it in many ways, e.g. a list of tuples
tuples = [tuple(x) for x in df.values]
# or export it as a list of dicts
dicts = df.to_dict().values()
Satu keuntungan besar adalah bahwa panda berurusan secara otomatis dengan baris tajuk.
Jika Anda belum pernah mendengar tentang Seaborn , saya sarankan untuk melihatnya.
Lihat juga: Bagaimana cara saya membaca dan menulis file CSV dengan Python?
Panda # 2
import pandas as pd
# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()
# Convert
dicts = df.to_dict('records')
Isi df adalah:
country population population_time EUR
0 Germany 82521653.0 2016-12-01 True
1 France 66991000.0 2017-01-01 True
2 Indonesia 255461700.0 2017-01-01 False
3 Ireland 4761865.0 NaT True
4 Spain 46549045.0 2017-06-01 True
5 Vatican NaN NaT True
Isi dari dikt adalah
[{'country': 'Germany', 'population': 82521653.0, 'population_time': Timestamp('2016-12-01 00:00:00'), 'EUR': True},
{'country': 'France', 'population': 66991000.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': True},
{'country': 'Indonesia', 'population': 255461700.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': False},
{'country': 'Ireland', 'population': 4761865.0, 'population_time': NaT, 'EUR': True},
{'country': 'Spain', 'population': 46549045.0, 'population_time': Timestamp('2017-06-01 00:00:00'), 'EUR': True},
{'country': 'Vatican', 'population': nan, 'population_time': NaT, 'EUR': True}]
Panda # 3
import pandas as pd
# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()
# Convert
lists = [[row[col] for col in df.columns] for row in df.to_dict('records')]
Isi dari lists
adalah:
[['Germany', 82521653.0, Timestamp('2016-12-01 00:00:00'), True],
['France', 66991000.0, Timestamp('2017-01-01 00:00:00'), True],
['Indonesia', 255461700.0, Timestamp('2017-01-01 00:00:00'), False],
['Ireland', 4761865.0, NaT, True],
['Spain', 46549045.0, Timestamp('2017-06-01 00:00:00'), True],
['Vatican', nan, NaT, True]]
csv
modul: docs.python.org/2/library/csv.html