import pandas as pddf = pd.read_csv("annotations.csv")[0:10]## 一 DataFrame,数据帧df，可以将其看作表格 ### 列：index，行：columns
df| seriesuid | coordX | coordY | coordZ | diameter_mm | |
|---|---|---|---|---|---|
| 0 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | -128.699421 | -175.319272 | -298.387506 | 5.651471 |
| 1 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | 103.783651 | -211.925149 | -227.121250 | 4.224708 |
| 2 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100398138793… | 69.639017 | -140.944586 | 876.374496 | 5.786348 |
| 3 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | -24.013824 | 192.102405 | -391.081276 | 8.143262 |
| 4 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 2.441547 | 172.464881 | -405.493732 | 18.545150 |
| 5 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 90.931713 | 149.027266 | -426.544715 | 18.208570 |
| 6 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 89.540769 | 196.405159 | -515.073322 | 16.381276 |
| 7 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100953483028… | 81.509646 | 54.957219 | -150.346423 | 10.362321 |
| 8 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408… | 105.055792 | 19.825260 | -91.247251 | 21.089619 |
| 9 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.104562737760… | -124.834262 | 127.247155 | -473.064479 | 10.465854 |
### 2 取其中某三列
pd.DataFrame(df,columns = ['seriesuid','coordX','coordY','coordZ'])| seriesuid | coordX | coordY | coordZ | |
|---|---|---|---|---|
| 0 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | -128.699421 | -175.319272 | -298.387506 |
| 1 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | 103.783651 | -211.925149 | -227.121250 |
| 2 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100398138793… | 69.639017 | -140.944586 | 876.374496 |
| 3 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | -24.013824 | 192.102405 | -391.081276 |
| 4 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 2.441547 | 172.464881 | -405.493732 |
| 5 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 90.931713 | 149.027266 | -426.544715 |
| 6 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 89.540769 | 196.405159 | -515.073322 |
| 7 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100953483028… | 81.509646 | 54.957219 | -150.346423 |
| 8 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408… | 105.055792 | 19.825260 | -91.247251 |
| 9 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.104562737760… | -124.834262 | 127.247155 | -473.064479 |
### 3 取其中某俩行
pd.DataFrame(df,index = [0,4])| seriesuid | coordX | coordY | coordZ | diameter_mm | |
|---|---|---|---|---|---|
| 0 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | -128.699421 | -175.319272 | -298.387506 | 5.651471 |
| 4 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 2.441547 | 172.464881 | -405.493732 | 18.545150 |
## 二 对DataFrame操作 ### 1 排序
df.sort_index(axis=1,ascending=True)| coordX | coordY | coordZ | diameter_mm | seriesuid | |
|---|---|---|---|---|---|
| 0 | -128.699421 | -175.319272 | -298.387506 | 5.651471 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… |
| 1 | 103.783651 | -211.925149 | -227.121250 | 4.224708 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… |
| 2 | 69.639017 | -140.944586 | 876.374496 | 5.786348 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100398138793… |
| 3 | -24.013824 | 192.102405 | -391.081276 | 8.143262 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… |
| 4 | 2.441547 | 172.464881 | -405.493732 | 18.545150 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… |
| 5 | 90.931713 | 149.027266 | -426.544715 | 18.208570 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… |
| 6 | 89.540769 | 196.405159 | -515.073322 | 16.381276 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… |
| 7 | 81.509646 | 54.957219 | -150.346423 | 10.362321 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100953483028… |
| 8 | 105.055792 | 19.825260 | -91.247251 | 21.089619 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408… |
| 9 | -124.834262 | 127.247155 | -473.064479 | 10.465854 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.104562737760… |
### 2 算数运算
df['corrd_X_Y'] = df['coordX']*df['coordY']df| seriesuid | coordX | coordY | coordZ | diameter_mm | corrd_X_Y | |
|---|---|---|---|---|---|---|
| 0 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | -128.699421 | -175.319272 | -298.387506 | 5.651471 | 22563.488788 |
| 1 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | 103.783651 | -211.925149 | -227.121250 | 4.224708 | -21994.365650 |
| 2 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100398138793… | 69.639017 | -140.944586 | 876.374496 | 5.786348 | -9815.242447 |
| 3 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | -24.013824 | 192.102405 | -391.081276 | 8.143262 | -4613.113389 |
| 4 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 2.441547 | 172.464881 | -405.493732 | 18.545150 | 421.081078 |
| 5 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 90.931713 | 149.027266 | -426.544715 | 18.208570 | 13551.304585 |
| 6 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 89.540769 | 196.405159 | -515.073322 | 16.381276 | 17586.268931 |
| 7 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100953483028… | 81.509646 | 54.957219 | -150.346423 | 10.362321 | 4479.543419 |
| 8 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408… | 105.055792 | 19.825260 | -91.247251 | 21.089619 | 2082.758414 |
| 9 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.104562737760… | -124.834262 | 127.247155 | -473.064479 | 10.465854 | -15884.804687 |
### 3 切片
df['diameter_mm']>60 False 1 False 2 False 3 True 4 True 5 True 6 True 7 True 8 True 9 True Name: diameter_mm, dtype: bool
df.loc[:,['coordX','coordY']]| coordX | coordY | |
|---|---|---|
| 0 | -128.699421 | -175.319272 |
| 1 | 103.783651 | -211.925149 |
| 2 | 69.639017 | -140.944586 |
| 3 | -24.013824 | 192.102405 |
| 4 | 2.441547 | 172.464881 |
| 5 | 90.931713 | 149.027266 |
| 6 | 89.540769 | 196.405159 |
| 7 | 81.509646 | 54.957219 |
| 8 | 105.055792 | 19.825260 |
| 9 | -124.834262 | 127.247155 |
df.iloc[[0,1],2:4]| coordY | coordZ | |
|---|---|---|
| 0 | -175.319272 | -298.387506 |
| 1 | -211.925149 | -227.121250 |
df[df['diameter_mm']>10]| seriesuid | coordX | coordY | coordZ | diameter_mm | corrd_X_Y | |
|---|---|---|---|---|---|---|
| 4 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 2.441547 | 172.464881 | -405.493732 | 18.545150 | 421.081078 |
| 5 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 90.931713 | 149.027266 | -426.544715 | 18.208570 | 13551.304585 |
| 6 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 89.540769 | 196.405159 | -515.073322 | 16.381276 | 17586.268931 |
| 7 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100953483028… | 81.509646 | 54.957219 | -150.346423 | 10.362321 | 4479.543419 |
| 8 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408… | 105.055792 | 19.825260 | -91.247251 | 21.089619 | 2082.758414 |
| 9 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.104562737760… | -124.834262 | 127.247155 | -473.064479 | 10.465854 | -15884.804687 |
### 4 合并
pd.concat([df,df,df],ignore_index=True) | seriesuid | coordX | coordY | coordZ | diameter_mm | corrd_X_Y | |
|---|---|---|---|---|---|---|
| 0 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | -128.699421 | -175.319272 | -298.387506 | 5.651471 | 22563.488788 |
| 1 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | 103.783651 | -211.925149 | -227.121250 | 4.224708 | -21994.365650 |
| 2 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100398138793… | 69.639017 | -140.944586 | 876.374496 | 5.786348 | -9815.242447 |
| 3 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | -24.013824 | 192.102405 | -391.081276 | 8.143262 | -4613.113389 |
| 4 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 2.441547 | 172.464881 | -405.493732 | 18.545150 | 421.081078 |
| 5 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 90.931713 | 149.027266 | -426.544715 | 18.208570 | 13551.304585 |
| 6 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 89.540769 | 196.405159 | -515.073322 | 16.381276 | 17586.268931 |
| 7 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100953483028… | 81.509646 | 54.957219 | -150.346423 | 10.362321 | 4479.543419 |
| 8 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408… | 105.055792 | 19.825260 | -91.247251 | 21.089619 | 2082.758414 |
| 9 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.104562737760… | -124.834262 | 127.247155 | -473.064479 | 10.465854 | -15884.804687 |
| 10 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | -128.699421 | -175.319272 | -298.387506 | 5.651471 | 22563.488788 |
| 11 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | 103.783651 | -211.925149 | -227.121250 | 4.224708 | -21994.365650 |
| 12 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100398138793… | 69.639017 | -140.944586 | 876.374496 | 5.786348 | -9815.242447 |
| 13 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | -24.013824 | 192.102405 | -391.081276 | 8.143262 | -4613.113389 |
| 14 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 2.441547 | 172.464881 | -405.493732 | 18.545150 | 421.081078 |
| 15 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 90.931713 | 149.027266 | -426.544715 | 18.208570 | 13551.304585 |
| 16 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 89.540769 | 196.405159 | -515.073322 | 16.381276 | 17586.268931 |
| 17 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100953483028… | 81.509646 | 54.957219 | -150.346423 | 10.362321 | 4479.543419 |
| 18 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408… | 105.055792 | 19.825260 | -91.247251 | 21.089619 | 2082.758414 |
| 19 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.104562737760… | -124.834262 | 127.247155 | -473.064479 | 10.465854 | -15884.804687 |
| 20 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | -128.699421 | -175.319272 | -298.387506 | 5.651471 | 22563.488788 |
| 21 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | 103.783651 | -211.925149 | -227.121250 | 4.224708 | -21994.365650 |
| 22 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100398138793… | 69.639017 | -140.944586 | 876.374496 | 5.786348 | -9815.242447 |
| 23 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | -24.013824 | 192.102405 | -391.081276 | 8.143262 | -4613.113389 |
| 24 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 2.441547 | 172.464881 | -405.493732 | 18.545150 | 421.081078 |
| 25 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 90.931713 | 149.027266 | -426.544715 | 18.208570 | 13551.304585 |
| 26 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 89.540769 | 196.405159 | -515.073322 | 16.381276 | 17586.268931 |
| 27 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100953483028… | 81.509646 | 54.957219 | -150.346423 | 10.362321 | 4479.543419 |
| 28 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408… | 105.055792 | 19.825260 | -91.247251 | 21.089619 | 2082.758414 |
| 29 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.104562737760… | -124.834262 | 127.247155 | -473.064479 | 10.465854 | -15884.804687 |
pd.merge(df,df,how='outer')| seriesuid | coordX | coordY | coordZ | diameter_mm | corrd_X_Y | |
|---|---|---|---|---|---|---|
| 0 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | -128.699421 | -175.319272 | -298.387506 | 5.651471 | 22563.488788 |
| 1 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222… | 103.783651 | -211.925149 | -227.121250 | 4.224708 | -21994.365650 |
| 2 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100398138793… | 69.639017 | -140.944586 | 876.374496 | 5.786348 | -9815.242447 |
| 3 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | -24.013824 | 192.102405 | -391.081276 | 8.143262 | -4613.113389 |
| 4 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 2.441547 | 172.464881 | -405.493732 | 18.545150 | 421.081078 |
| 5 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 90.931713 | 149.027266 | -426.544715 | 18.208570 | 13551.304585 |
| 6 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100621383016… | 89.540769 | 196.405159 | -515.073322 | 16.381276 | 17586.268931 |
| 7 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.100953483028… | 81.509646 | 54.957219 | -150.346423 | 10.362321 | 4479.543419 |
| 8 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408… | 105.055792 | 19.825260 | -91.247251 | 21.089619 | 2082.758414 |
| 9 | 1.3.6.1.4.1.14519.5.2.1.6279.6001.104562737760… | -124.834262 | 127.247155 | -473.064479 | 10.465854 | -15884.804687 |
5 合并文件夹下所有同类型的csv的小例子
csv_files = glob.glob('/*/*/*.csv')
df = df = pd.DataFrame(columns=['seriesuid', 'coordX', 'coordY', 'coordZ', 'diameter_mm','des'])
for csv in csv_files:
df = pd.merge(df,pd.read_csv(csv),how='outer')
df_to_save = pd.DataFrame(df,columns=['seriesuid', 'coordX', 'coordY', 'coordZ', 'diameter_mm'])
df_to_save.to_csv('annotations.csv',index=False)
版权声明：本文为qq_16949707原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。