300字范文 > python 37 pandas操作csv文件小结 csv文件合并

python 37 pandas操作csv文件小结 csv文件合并

时间：2018-12-23 04:48:09

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：columnsdf 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"]>6 0 False 1 False 2 False 3 True 4 True 5 True 6 True 7 True 8 True 9 True Name: diameter_mm, dtype: booldf.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