Python 数据清洗
Adel Nehme
VP of AI Curriculum, DataCamp
垃圾进，垃圾出
str
int
float
bool
datetime
category
# Import CSV file and output header sales = pd.read_csv('sales.csv') sales.head(2)
SalesOrderID Revenue Quantity 0 43659 23153$ 12 1 43660 1457$ 2
# Get data types of columns sales.dtypes
SalesOrderID int64 Revenue object Quantity int64 dtype: object
# Get DataFrame information sales.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 31465 entries, 0 to 31464 Data columns (total 3 columns): SalesOrderID 31465 non-null int64 Revenue 31465 non-null object Quantity 31465 non-null int64 dtypes: int64(2), object(1) memory usage: 737.5+ KB
# Print sum of all Revenue column sales['Revenue'].sum()
'23153$1457$36865$32474$472$27510$16158$5694$6876$40487$807$6893$9153$6895$4216..
# Remove $ from Revenue column sales['Revenue'] = sales['Revenue'].str.strip('$') sales['Revenue'] = sales['Revenue'].astype('int')
# Verify that Revenue is now an integer assert sales['Revenue'].dtype == 'int'
DNT_CURLLY_TAG_3
# This will pass assert 1+1 == 2
# This will not pass assert 1+1 == 3
AssertionError Traceback (most recent call last) assert 1+1 == 3 AssertionError:
... marriage_status ... ... 3 ... ... 1 ... ... 2 ...
0 = 从未结婚 1 = 已婚 2 = 分居 3 = 离婚
0
1
2
3
df['marriage_status'].describe()
marriage_status ... mean 1.4 std 0.20 min 0.00 50% 1.8 ...
# Convert to categorical df["marriage_status"] = df["marriage_status"].astype('category') df.describe()
# Convert to categorical df["marriage_status"] = df["marriage_status"].astype('category')
df.describe()
marriage_status count 241 unique 4 top 1 freq 120
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