一、创建dataframe
- 法一
dat = ({'id':[1,2,3], 'string': ['a', 'b','c']})
- 法二(若已有现成的list)
dat = pd.DataFrame([n_clusters_start, score], columns = ["分类数", "得分"])
exclamationCount = lambda text: sum([1 for x in text if x == '!'])
EC = tweet.apply(lambda x:exclamationCount(x))
EC = EC.tolist()
questionMarkCount = lambda text: sum([1 for x in text if x == '?'])
QC = tweet.apply(lambda x:questionMarkCount(x))
QC = QC.tolist()
dat = pd.DataFrame({'EC':EC,'QC':QC})
eachLetterCount = lambda text,letter: sum([1 for x in text.lower() if x == letter])
FList = []
pattern = 'abcdefghijklmnopqrstuvwxyz'
j=0
for i in pattern:
F = tweet.apply(lambda x:eachLetterCount(x,i))
F = F.tolist()
FList.append(F)
res = pd.DataFrame(FList)
res = res.transpose()
pattern = 'abcdefghijklmnopqrstuvwxyz'
name = []
for i in pattern:
name.append("freqOf " + i)
res.columns = name
二、数据框拼接(ignore_index = True, 重新分配索引)
# 两种方式,concat、 append都可以
result = pd.concat([result1, result2], ignore_index = True) # 默认axis = 0 -> 粘贴行
result = result1.append(result2, ignore_index = True) # 粘贴行
RF_eval = pd.concat([RF_eval, eval_raw], axis = 1) # 粘贴列
三、删掉列
RF_eval.drop(['raw'], axis = 1, inplace = True)
四、删掉行
dat = dat.drop(0)
五、提取行索引
index0 = res.index[res['label'] == 0].tolist()
X0 = X[index0] # X为矩阵