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import pandas as pd
pd.__version__
arr = [0, 1, 2, 3, 4]
df = pd.Series(arr) # 如果不指定索引,则默认从 0 开始
df
d = {'a':1,'b':2,'c':3,'d':4,'e':5}
df = pd.Series(d)
df
dates = pd.date_range('today',periods=6) # 定义时间序列作为 index
num_arr = np.random.randn(6,4) # 传入 numpy 随机数组
columns = ['A','B','C','D'] # 将列表作为列名
df1 = pd.DataFrame(num_arr, index = dates, columns = columns)
df1
# df = pd.read_csv('test.csv', encoding='gbk, sep=';')
import numpy as np
data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],
'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],
'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}
labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
df = pd.DataFrame(data, index=labels)
df
df.info()
# 方法二
# df.describe()
df.iloc[:3]
# 方法二
#df.head(3)
df.loc[:, ['animal', 'age']]
# 方法二
# df[['animal', 'age']]
df.loc[df.index[[3, 4, 8]], ['animal', 'age']]
df[df['age'] > 3]
df[df['age'].isnull()]
df[(df['age']>2) & (df['age']>4)]
#
#df[df['age'].between(2, 4)]
df.loc['f', 'age'] = 1.5
df['visits'].sum()
df.groupby('animal')['age'].mean()
#插入
df.loc['k'] = [5.5, 'dog', 'no', 2]
# 删除
df = df.drop('k')
df
df['animal'].value_counts()
df.sort_values(by=['age', 'visits'], ascending=[False, True])
df['priority'] = df['priority'].map({'yes': True, 'no': False})
df
df['animal'] = df['animal'].replace('snake', 'python')
df
df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')
df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})
print(df)
df1 = df.loc[df['A'].shift() != df['A']]
# 方法二
# df1 = df.drop_duplicates(subset='A')
print(df1)
df = pd.DataFrame(np.random.random(size=(5, 3)))
print(df)
df1 = df.sub(df.mean(axis=1), axis=0)
print(df1)
df = pd.DataFrame(np.random.random(size=(5, 5)), columns=list('abcde'))
print(df)
df.sum().idxmin()
df = pd.DataFrame({'A': list('aaabbcaabcccbbc'),
'B': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})
print(df)
df1 = df.groupby('A')['B'].nlargest(3).sum(level=0)
print(df1)
df = pd.DataFrame({'A': [1,2,11,11,33,34,35,40,79,99],
'B': [1,2,11,11,33,34,35,40,79,99]})
print(df)
df1 = df.groupby(pd.cut(df['A'], np.arange(0, 101, 10)))['B'].sum()
print(df1)
df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})
izero = np.r_[-1, (df['X'] == 0).to_numpy().nonzero()[0]] # 标记0的位置
idx = np.arange(len(df))
df['Y'] = idx - izero[np.searchsorted(izero - 1, idx) - 1]
print(df)
# 方法二
# x = (df['X'] != 0).cumsum()
# y = x != x.shift()
# df['Y'] = y.groupby((y != y.shift()).cumsum()).cumsum()
# 方法三
# df['Y'] = df.groupby((df['X'] == 0).cumsum()).cumcount()
#first_zero_idx = (df['X'] == 0).idxmax()
# df['Y'].iloc[0:first_zero_idx] += 1
df = pd.DataFrame(np.random.random(size=(5, 3)))
print(df)
df.unstack().sort_values()[-3:].index.tolist()
df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'),
'vals': [-12,345,3,1,45,14,4,-52,54,23,-235,21,57,3,87]})
print(df)
def replace(group):
mask = group<0
group[mask] = group[~mask].mean()
return group
df['vals'] = df.groupby(['grps'])['vals'].transform(replace)
print(df)
df = pd.DataFrame({'group': list('aabbabbbabab'),
'value': [1, 2, 3, np.nan, 2, 3, np.nan, 1, 7, 3, np.nan, 8]})
print(df)
g1 = df.groupby(['group'])['value']
g2 = df.fillna(0).groupby(['group'])['value']
s = g2.rolling(3, min_periods=1).sum() / g1.rolling(3, min_periods=1).count()
s.reset_index(level=0, drop=True).sort_index()
dti = pd.date_range(start='2015-01-01', end='2015-12-31', freq='B')
s = pd.Series(np.random.rand(len(dti)), index=dti)
s.head(10)
s[s.index.weekday == 2].sum()
s.resample('M').mean()
s.groupby(pd.Grouper(freq='4M')).idxmax()
pd.date_range('2015-01-01', '2016-12-31', freq='WOM-3THU')
df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm',
'Budapest_PaRis', 'Brussels_londOn'],
'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],
'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],
'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )',
'12. Air France', '"Swiss Air"']})
df
df['FlightNumber'] = df['FlightNumber'].interpolate().astype(int)
df
temp = df.From_To.str.split('_', expand=True)
temp.columns = ['From', 'To']
df = df.join(temp)
df = df.drop('From_To', axis=1)
df
df['From'] = df['From'].str.capitalize()
df['To'] = df['To'].str.capitalize()
df
df['Airline'] = df['Airline'].str.extract('([a-zA-Z\s]+)', expand=False).str.strip()
df
delays = df['RecentDelays'].apply(pd.Series)
delays.columns = ['delay_{}'.format(n) for n in range(1, len(delays.columns)+1)]
df = df.drop('RecentDelays', axis=1).join(delays)
df
letters = ['A', 'B', 'C']
numbers = list(range(4))
mi = pd.MultiIndex.from_product([letters, numbers])
s = pd.Series(np.random.rand(12), index=mi)
s
s.index.is_lexsorted()
# 方法二
# s.index.lexsort_depth == s.index.nlevels
s.loc[:, [1, 3]]
s.loc[pd.IndexSlice[:'B', 2:]]
# 方法二
# s.loc[slice(None, 'B'), slice(2, None)]
s.sum(level=0)
#方法二
#s.unstack().sum(axis=0)
new_s = s.swaplevel(0, 1)
print(new_s)
print(new_s.index.is_lexsorted())
new_s = new_s.sort_index()
print(new_s)
import matplotlib.pyplot as plt
df = pd.DataFrame({"xs":[1,5,2,8,1], "ys":[4,2,1,9,6]})
plt.style.use('ggplot')
df.plot.scatter("xs", "ys", color = "black", marker = "x")
df = pd.DataFrame({"productivity":[5,2,3,1,4,5,6,7,8,3,4,8,9],
"hours_in" :[1,9,6,5,3,9,2,9,1,7,4,2,2],
"happiness" :[2,1,3,2,3,1,2,3,1,2,2,1,3],
"caffienated" :[0,0,1,1,0,0,0,0,1,1,0,1,0]})
df.plot.scatter("hours_in", "productivity", s = df.happiness * 100, c = df.caffienated)
df = pd.DataFrame({"revenue":[57,68,63,71,72,90,80,62,59,51,47,52],
"advertising":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],
"month":range(12)})
ax = df.plot.bar("month", "revenue", color = "green")
df.plot.line("month", "advertising", secondary_y = True, ax = ax)
ax.set_xlim((-1,12));
原文地址:https://www.kesci.com/home/project/5ddc974ef41512002cec1dca