目录

一、构建表格二、调用列对象和其中的属性三、其中的属性debug四、怎么获得行

DataFrame 是一个表格型的数据结构，它含有一组有序的列，每列可以是不同的值类型（数值、字符串、布尔型值）。DataFrame 既有行索引也有列索引，它可以被看做由 Series 组成的字典（共同用一个索引）。

利用pandas.DataFrame可以构建表格，通过列标属性调用列对象

一、构建表格

举例

(相关资料图)

import pandas as pd
x = [
        ["PyTorch", "-", ".pt", True, True],
        ["TorchScript", "torchscript", ".torchscript", True, True],
        ["ONNX", "onnx", ".onnx", True, True],
        ["OpenVINO", "openvino", "_openvino_model", True, False],
        ["TensorRT", "engine", ".engine", False, True],
        ["CoreML", "coreml", ".mlmodel", True, False],
        ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True],
        ["TensorFlow GraphDef", "pb", ".pb", True, True],
        ["TensorFlow Lite", "tflite", ".tflite", True, False],
        ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False],
        ["TensorFlow.js", "tfjs", "_web_model", False, False],
        ["PaddlePaddle", "paddle", "_paddle_model", True, True],]
df1 = pd.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"])
df2 = pd.DataFrame(x, index=list(["a","b","c","d","e","f","g","q","w","e","r","t"]),columns=["Format", "Argument", "Suffix", "CPU", "GPU"])
print(df1)
print("=======================================")
print(df2)

输出结果

Format Argument Suffix CPU GPU
0 PyTorch - .pt True True
1 TorchScript torchscript .torchscript True True
2 ONNX onnx .onnx True True
3 OpenVINO openvino _openvino_model True False
4 TensorRT engine .engine False True
5 CoreML coreml .mlmodel True False
6 TensorFlow SavedModel saved_model _saved_model True True
7 TensorFlow GraphDef pb .pb True True
8 TensorFlow Lite tflite .tflite True False
9 TensorFlow Edge TPU edgetpu _edgetpu.tflite False False
10 TensorFlow.js tfjs _web_model False False
11 PaddlePaddle paddle _paddle_model True True
=======================================
Format Argument Suffix CPU GPU
a PyTorch - .pt True True
b TorchScript torchscript .torchscript True True
c ONNX onnx .onnx True True
d OpenVINO openvino _openvino_model True False
e TensorRT engine .engine False True
f CoreML coreml .mlmodel True False
g TensorFlow SavedModel saved_model _saved_model True True
q TensorFlow GraphDef pb .pb True True
w TensorFlow Lite tflite .tflite True False
e TensorFlow Edge TPU edgetpu _edgetpu.tflite False False
r TensorFlow.js tfjs _web_model False False
t PaddlePaddle paddle _paddle_model True True

可以看出index参数为行标设置，columns为列标设置，且都需为列表形式，长度都需要与给出的列表横列数量一致（例子中的x）。

二、调用列对象和其中的属性

import pandas as pd
x = [
        ["PyTorch", "-", ".pt", True, True],
        ["TorchScript", "torchscript", ".torchscript", True, True],
        ["ONNX", "onnx", ".onnx", True, True],
        ["OpenVINO", "openvino", "_openvino_model", True, False],
        ["TensorRT", "engine", ".engine", False, True],
        ["CoreML", "coreml", ".mlmodel", True, False],
        ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True],
        ["TensorFlow GraphDef", "pb", ".pb", True, True],
        ["TensorFlow Lite", "tflite", ".tflite", True, False],
        ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False],
        ["TensorFlow.js", "tfjs", "_web_model", False, False],
        ["PaddlePaddle", "paddle", "_paddle_model", True, True],]
df1 = pd.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"])
df2 = pd.DataFrame(x, index=list(["a","b","c","d","e","f","g","q","w","e","r","t"]),columns=["Format", "Argument", "Suffix", "CPU", "GPU"])
# print(df1)
# print("=======================================")
# print(df2)
print(df1.Suffix)
print("=====================================")
print(df2.Format)

结合这一中的输出表看，其输出结果如下

0 .pt
1 .torchscript
2 .onnx
3 _openvino_model
4 .engine
5 .mlmodel
6 _saved_model
7 .pb
8 .tflite
9 _edgetpu.tflite
10 _web_model
11 _paddle_model
Name: Suffix, dtype: object
=====================================
a PyTorch
b TorchScript
c ONNX
d OpenVINO
e TensorRT
f CoreML
g TensorFlow SavedModel
q TensorFlow GraphDef
w TensorFlow Lite
e TensorFlow Edge TPU
r TensorFlow.js
t PaddlePaddle
Name: Format, dtype: object

可以看到 输出的是一个 列的类实例，若继续调用这个列中的每个元素，可以通过下列语句实现

print(df1.Suffix[0])
print("=====================================")
print(df2.Format[1])
print("=====================================")

即通过索引调用，输出为

.pt
=====================================
TorchScript
=====================================

或者通过该属性所在的行标进行调用

print(df2.Format["a"])

输出为

PyTorch

三、其中的属性debug

四、怎么获得行

目前还不清楚，上面的debug显示其不包含具有 行信息的属性，不过可以通过 values这个属性来调用行，

values也是个类实例，其值为numpy矩阵，所以通过矩阵形式调用行，例如

print(df1.values[0, :])
>>["PyTorch" "-" ".pt" True True]

到此这篇关于pandas库中 DataFrame的用法的文章就介绍到这了,更多相关pandas库DataFrame用法内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！

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