python中用pandas库怎样创建和读写csv文件-群英

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简单展示如何利用python中的pandas库创建、读取、修改CSV数据文件

1 写入CSV文件

import numpy as np
import pandas as pd

# -----create an initial numpy array----- #
data = np.zeros((8,4))
# print(data.dtype)
# print(type(data))
# print(data.shape)

# -----from array to dataframe----- #
df = pd.DataFrame(data)
# print(type(df))
# print(df.shape)
# print(df)

# -----edit columns and index----- #
df.columns = ['A', 'B', 'C', 'D']
df.index = range(data.shape[0])
df.info()

# -----save dataframe as csv----- #
csv_save_path='./data_.csv'
df.to_csv(csv_save_path, sep=',', index=False, header=True)

# -----check----- #
df = pd.read_csv(csv_save_path)
print('-' * 25)
print(df)

输出如下：

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 4 columns):
A 8 non-null float64
B 8 non-null float64
C 8 non-null float64
D 8 non-null float64
dtypes: float64(4)
memory usage: 336.0 bytes
-------------------------
A B C D
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 0.0
5 0.0 0.0 0.0 0.0
6 0.0 0.0 0.0 0.0
7 0.0 0.0 0.0 0.0

2 读取CSV文件

import pandas as pd
import numpy as np

csv_path = './data_.csv'

# -----saved as dataframe----- #
data = pd.read_csv(csv_path)
# ---if index is given in csv file, you can use next line of code to replace the previous one---
# data = pd.read_csv(csv_path, index_col=0)
print(type(data))
print(data)
print(data.shape)

# -----saved as array----- #
data_ = np.array(data)
# data_ = data.values
print(type(data_))
print(data_)
print(data_.shape)

输出如下：

<class 'pandas.core.frame.DataFrame'>
A B C D
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 0.0
5 0.0 0.0 0.0 0.0
6 0.0 0.0 0.0 0.0
7 0.0 0.0 0.0 0.0
(8, 4)
<class 'numpy.ndarray'>
[[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]]
(8, 4)

3 修改CSV文件

import pandas as pd
import numpy as np

csv_path = './data_.csv'
df = pd.read_csv(csv_path)

# -----edit columns and index----- #
df.columns = ['X1', 'X2', 'X3', 'Y']
df.index = range(df.shape[0])
# df.index = [i+1 for i in range(df.shape[0])]

# -----columns operations----- #
Y = df['Y']
df['X4'] = [4 for i in range(df.shape[0])]        # add
df['X5'] = [5 for i in range(df.shape[0])]
# print(df)
df.drop(columns='Y', inplace=True)                # delete
# print(df)
df['X1'] = [i+1 for i in range(df.shape[0])]      # correct --(1)
# df.iloc[:df.shape[0], 0] = [i+1 for i in range(df.shape[0])]
                                                  # correct --(2)
# print(df)
df['Y'] = Y_temp  
# print(df)

# -----rows operations----- #
df.loc[df.shape[0]] = [i+2 for i in range(6)]     # add
# print(df)
df.drop(index=4, inplace=True)                    # delete
# print(df)
df.loc[0] = [i+1 for i in range(df.shape[1])]     # correct
# print(df)

# -----edit index again after rows operations!!!----- #
df.index = range(df.shape[0])

# -----save dataframe as csv----- #
csv_save_path='./data_copy.csv'
df.to_csv(csv_save_path, sep=',', index=False, header=True)

print(df)

输出如下：

X1 X2 X3 X4 X5 Y
0 1.0 2.0 3.0 4 5 6.0
1 2.0 0.0 0.0 4 5 0.0
2 3.0 0.0 0.0 4 5 0.0
3 4.0 0.0 0.0 4 5 0.0
4 6.0 0.0 0.0 4 5 0.0
5 7.0 0.0 0.0 4 5 0.0
6 8.0 0.0 0.0 4 5 0.0
7 2.0 3.0 4.0 5 6 7.0