pandas常用参数
数值显示格式：当数值很大的时候pandas默认会使用科学计数法
# float数据类型以{:.4f}格式显示，即显示完整数据且保留后四位pd.options.display.float_format = '{:.4f}'.format
pandas常用函数
1. 统计
# descibe方法会计算每列数据对象是数值的count, mean, std, min, max, 以及一定比率的值df.describe() Open Close High Low Volumecount 641.0000 641.0000 641.0000 641.0000 641.0000mean 10.7862 9.7927 9.8942 9.6863 833968.6162std 1.5962 1.6021 1.6620 1.5424 607731.6934min 8.6580 7.6100 7.7770 7.4990 153901.000025% 9.7080 8.7180 8.7760 8.6500 418387.000050% 10.0770 9.0960 9.1450 8.9990 627656.000075% 11.8550 10.8350 10.9920 10.7270 1039297.0000max 15.9090 14.8600 14.9980 14.4470 4262825.0000 # 单独统计Open列的平均值df.Open.mean()10.786248049922001 # 查看居于95%的值, 默认线性拟合df.Open.quantile(0.95)14.187 # 查看Open列每个值出现的次数df.Open.value_counts().head() 9.8050 129.8630 109.8440 109.8730 109.8830 8Name: Open, dtype: int64
2. 缺失值处理
删除或者填充缺失值。
# 删除含有NaN的任意行df.dropna(how='any') # 删除含有NaN的任意列df.dropna(how='any', axis=1) # 将NaN的值改为5df.fillna(value=5)
3. 排序
按行或者列排序, 默认也不修改源数据。
# 按列排序df.sort_index(axis=1).head() Close Code Date High Low Open Volumedate2015-12-24 9.8230 000001 2015-12-24 9.9980 9.7440 10.9190 640229.00002015-12-25 1.0000 000001 2015-12-25 1.0000 9.8150 10.8550 399845.00002015-12-28 1.0000 000001 2015-12-28 1.0000 9.5370 10.8950 822408.00002015-12-29 9.6240 000001 2015-12-29 9.6320 9.5290 10.5450 619802.00002015-12-30 9.6320 000001 2015-12-30 9.6400 9.5130 10.6240 532667.0000 # 按行排序，不递增df.sort_index(ascending=False).head() Date Open Close High Low Volume Code date2018-08-08 2018-08-08 10.1600 9.1100 9.1600 9.0900 153901.0000 0000012018-08-07 2018-08-07 9.9600 9.1700 9.1700 8.8800 690423.0000 0000012018-08-06 2018-08-06 9.9400 8.9400 9.1100 8.8900 554010.0000 0000012018-08-03 2018-08-03 9.9300 8.9100 9.1000 8.9100 476546.0000 0000012018-08-02 2018-08-02 10.1300 8.9400 9.1500 8.8800 931401.0000 000001
安装某一列的值排序
# 按照Open列的值从小到大排序df.sort_values(by="Open") Date Open Close High Low Volume Codedate 2016-03-01 2016-03-01 8.6580 7.7220 7.7770 7.6260 377910.0000 0000012016-02-15 2016-02-15 8.6900 7.7930 7.8410 7.6820 278499.0000 0000012016-01-29 2016-01-29 8.7540 7.9610 8.0240 7.7140 544435.0000 0000012016-03-02 2016-03-02 8.7620 8.0400 8.0640 7.7380 676613.0000 0000012016-02-26 2016-02-26 8.7770 7.7930 7.8250 7.6900 392154.0000 000001
4. 合并
concat, 按照行方向或者列方向合并。
# 分别取0到2行，2到4行，4到9行组成一个列表，通过concat方法按照axis=0，行方向合并, axis参数不指定，默认为0split_rows = [df.iloc[0:2,:],df.iloc[2:4,:], df.iloc[4:9]]pd.concat(split_rows) Date Open Close High Low Volume Codedate2015-12-24 2015-12-24 10.9190 9.8230 9.9980 9.7440 640229.0000 0000012015-12-25 2015-12-25 10.8550 1.0000 1.0000 9.8150 399845.0000 0000012015-12-28 2015-12-28 10.8950 1.0000 1.0000 9.5370 822408.0000 0000012015-12-29 2015-12-29 10.5450 9.6240 9.6320 9.5290 619802.0000 0000012015-12-30 2015-12-30 10.6240 9.6320 9.6400 9.5130 532667.0000 0000012015-12-31 2015-12-31 10.6320 9.5450 9.6560 9.5370 491258.0000 0000012016-01-04 2016-01-04 10.5530 8.9950 9.5770 8.9400 563497.0000 0000012016-01-05 2016-01-05 9.9720 9.0750 9.2100 8.8760 663269.0000 0000012016-01-06 2016-01-06 10.0910 9.1790 9.2020 9.0670 515706.0000 000001 # 分别取2到3列，3到5列，5列及以后列数组成一个列表，通过concat方法按照axis=1，列方向合并split_columns = [df.iloc[:,1:2], df.iloc[:,2:4], df.iloc[:,4:]]pd.concat(split_columns, axis=1).head() Open Close High Low Volume Code date2015-12-24 10.9190 9.8230 9.9980 9.7440 640229.0000 0000012015-12-25 10.8550 1.0000 1.0000 9.8150 399845.0000 0000012015-12-28 10.8950 1.0000 1.0000 9.5370 822408.0000 0000012015-12-29 10.5450 9.6240 9.6320 9.5290 619802.0000 0000012015-12-30 10.6240 9.6320 9.6400 9.5130 532667.0000 000001
追加行, 相应的还有insert, 插入插入到指定位置
# 将第一行追加到最后一行df.append(df.iloc[0,:], ignore_index=True).tail() Date Open Close High Low Volume Code637 2018-08-03 9.9300 8.9100 9.1000 8.9100 476546.0000 000001638 2018-08-06 9.9400 8.9400 9.1100 8.8900 554010.0000 000001639 2018-08-07 9.9600 9.1700 9.1700 8.8800 690423.0000 000001640 2018-08-08 10.1600 9.1100 9.1600 9.0900 153901.0000 000001641 2015-12-24 10.9190 9.8230 9.9980 9.7440 640229.0000 000001
5. 对象复制
由于dataframe是引用对象，所以需要显示调用copy方法用以复制整个dataframe对象。
