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Python，Pandas：将DataFrame的内容写入文本文件（python将dataframe写入excel）

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对于想了解Python，Pandas：将DataFrame的内容写入文本文件的读者，本文将是一篇不可错过的文章，我们将详细介绍python将dataframe写入excel，并且为您提供关于pandas-19 DataFrame读取写入文件的方法、pandas：DataFrame的属性和pandas文件的操作、Python / Pandas：如何将字符串列表与DataFrame列匹配、Python Pandas -- DataFrame的有价值信息。

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Python，Pandas：将DataFrame的内容写入文本文件（python将dataframe写入excel）

Python，Pandas：将DataFrame的内容写入文本文件（python将dataframe写入excel）

我有这样的熊猫DataFrame

        X    Y  Z    Value 0      18   55  1      70   1      18   55  2      67 2      18   57  2      75     3      18   58  1      35  4      19   54  2      70

我想将此数据写入如下所示的文本文件：

18 55 1 70   18 55 2 67 18 57 2 75     18 58 1 35  19 54 2 70

我已经尝试过类似的东西

f = open(writePath, ''a'')f.writelines([''\n'', str(data[''X'']), '' '', str(data[''Y'']), '' '', str(data[''Z'']), '' '', str(data[''Value''])])f.close()

但它不起作用。这该怎么做？

答案1

小编典典

您可以只使用np.savetxt和访问np属性.values：

np.savetxt(r''c:\data\np.txt'', df.values, fmt=''%d'')

产量：

18 55 1 7018 55 2 6718 57 2 7518 58 1 3519 54 2 70

或to_csv：

df.to_csv(r''c:\data\pandas.txt'', header=None, index=None, sep='' '', mode=''a'')

请注意，np.savetxt您必须传递通过追加模式创建的文件句柄。

pandas-19 DataFrame读取写入文件的方法

pandas-19 DataFrame读取写入文件的方法

pandas-19 DataFrame读取写入文件的方法

DataFrame有非常丰富的IO方法，比如DataFrame读写csv文件excel文件等等，操作很简单。下面在代码中标记出来一些常用的读写操作方法，需要的时候查询一下该方法就可以了。

df1.to_csv(‘df1.csv’) # 默认会把 index 也当成一列写入到文件中 df1.to_csv(‘df2.csv’, index=False) # 如果不想显示索引，可以添加第二个参数 index = False df1.to_json(‘df1.csv’) 写入到json文件 df1.to_excel(‘df1.xlsx’) 写入到excel文件

更多例子参考如下：

import pandas as pd
from pandas import Series, DataFrame

import webbrowser
link = ''http://pandas.pydata.org/pandas-docs/version/0.20/io.html''
webbrowser.open(link)

df1 = pd.read_clipboard()
print(df1)
''''''
   Format Type      Data Description          Reader        Writer
0         text                   CSV        read_csv        to_csv
1         text                  JSON       read_json       to_json
2         text                  HTML       read_html       to_html
3         text       Local clipboard  read_clipboard  to_clipboard
4       binary              MS Excel      read_excel      to_excel
5       binary           HDF5 Format        read_hdf        to_hdf
6       binary        Feather Format    read_feather    to_feather
7       binary               Msgpack    read_msgpack    to_msgpack
8       binary                 Stata      read_stata      to_stata
9       binary                   SAS        read_sas              
10      binary  Python Pickle Format     read_pickle     to_pickle
11         SQL                   SQL        read_sql        to_sql
12         SQL      Google Big Query        read_gbq        to_gbq
''''''

df1.to_csv(''df1.csv'') # 默认会把 index 也当成一列写入到文件中

df1.to_csv(''df2.csv'', index=False) # 如果不想显示索引，可以添加第二个参数 index = False

print(df1.to_json())

df1.to_excel(''df1.xlsx'')

pandas：DataFrame的属性和pandas文件的操作

pandas：DataFrame的属性和pandas文件的操作

　　先将模块导入文件中

import pandas as pd

1、DataFrame的属性

df = pd.DataFrame(
    data={
        "name": ["zs", "ls", "ww", "zl"],
        "age": [18, 19, 29, 11],
        "score": [92.5, 93, 97, 65]
    },
    index=["stu_1", "stu_2", "stu_3", "stu_4"]
)

　　创建一个df

　　将df和df的类型打印看看：

（1）df的索引属性

print("获取df 的行索引名称：\n", df.index)
print("获取df 的列索引名称：\n", df.columns)

 （2）df的values属性

print("获取df 的values:\n", df.values)
print("获取df 的values的类型:\n", type(df.values))

（3）df的形状和维度属性

print("获取df 的形状：\n", df.shape)
print("获取df 的维度：\n", df.ndim)

（4）df的元素个数和元素数据类型

print("获取df 的元素个数：\n", df.size)
print("获取df 的元素数据类型：\n", df.dtypes)

 2、pandas文件的操作

（1）使用read_table()方法读取文件

info = pd.read_table(
    filepath_or_buffer="./meal_order_info.csv",
    sep=",",
    header="infer",  # 自动识别
    # header=None, # 不指定列名
    # header=0,  # 指定第0行位 列索引名称
    encoding="ansi",
    # index_col= 0 # 把第0列设置为行索引名称
    # nrows=3，
    # usecols=[0,1],
    # names=["01","07"]， # 可以自己设置列名
    # usecols=["info_id","emp_id"]
)

　　csv文件：以逗号“,”为分割符的文本文件，filepath_or_buffer：文件路径+文件名，sep/delimiter：分隔符，header="infer"：自动识别索引列名称，names：可以自行指定列名称，index_col：可以指定哪一列、哪几列作为行索引名称，usecol：可以自行获取指定的列，encoding：设置编码，nrows：可以指定读取的行数

（2）使用read_csv()方法读取文件

info = pd.read_csv("./meal_order_info.csv",encoding="ansi")

　　具体参数参考read_table()方法

（3）使用read_excel()方法读取文件

users = pd.read_excel(
    "./users.xlsx",
    sheetname=0,
    parse_cols=[0, 1],  # 在某些版本起作用，
)

　　使用read_excel()方法读取excel文件（.xlsx、.xls结尾），参数1：文件路径+文件名，sheetname：表的序号，index_col：可以指定哪一列、哪几列作为行索引名称，parse_cols：读取指定的列

（4）使用to_csv()方法保存文件

info.to_csv("./info_save.csv",index=False,mode="a")

　　info：DataFrame变量，index：是否保存索引，mode：保存的模式

Python / Pandas：如何将字符串列表与DataFrame列匹配

Python / Pandas：如何将字符串列表与DataFrame列匹配

我想比较两个columnnDescription和Employer。我想查看是否Employer在该Description列中找到任何关键字。我已将该Employer列分解为单词，并转换为列表。现在，我想看看这些单词中是否有相应的Description列。

输入样例：

print(df.head(25))          Date           Description   Amount  AutoNumber  \0    3/17/2015  WW120 TFR?FR xxx8690   140.00       49246   2    3/13/2015  JX154 TFR?FR xxx8690   150.00       49246   5     3/6/2015   CANSEL SURVEY E PAY  1182.08       49246   9     3/2/2015  UE200 TFR?FR xxx8690   180.00       49246   10   2/27/2015  JH401 TFR?FR xxx8690   400.00       49246   11   2/27/2015   CANSEL SURVEY E PAY   555.62       49246   12   2/25/2015  HU204 TFR?FR xxx8690   200.00       49246   13   2/23/2015  UQ263 TFR?FR xxx8690   102.00       49246   14   2/23/2015  UT460 TFR?FR xxx8690   200.00       49246   15   2/20/2015   CANSEL SURVEY E PAY  1222.05       49246   17   2/17/2015  UO414 TFR?FR xxx8690   250.00       49246   19   2/11/2015  HI540 TFR?FR xxx8690   130.00       49246   20   2/11/2015  HQ010 TFR?FR xxx8690   177.00       49246   21   2/10/2015  WU455 TFR?FR xxx8690   200.00       49246   22    2/6/2015  JJ500 TFR?FR xxx8690   301.00       49246   23    2/6/2015   CANSEL SURVEY E PAY  1182.08       49246   24    2/5/2015  IR453 TFR?FR xxx8690   168.56       49246   26    2/2/2015  RQ574 TFR?FR xxx8690   500.00       49246   27    2/2/2015  UT022 TFR?FR xxx8690   850.00       49246   28  12/31/2014  HU521 TFR?FR xxx8690   950.17       49246                   Employer  0   Cansel Survey Equipment  2   Cansel Survey Equipment  5   Cansel Survey Equipment  9   Cansel Survey Equipment  10  Cansel Survey Equipment  11  Cansel Survey Equipment  12  Cansel Survey Equipment  13  Cansel Survey Equipment  14  Cansel Survey Equipment  15  Cansel Survey Equipment  17  Cansel Survey Equipment  19  Cansel Survey Equipment  20  Cansel Survey Equipment  21  Cansel Survey Equipment  22  Cansel Survey Equipment  23  Cansel Survey Equipment  24  Cansel Survey Equipment  26  Cansel Survey Equipment  27  Cansel Survey Equipment  28  Cansel Survey Equipment

我尝试过类似的方法，但似乎不起作用。

df[''Text_Search''] = df[''Employer''].apply(lambda x: x.split(" "))df[''Match''] = np.where(df[''Description''].str.contains("|".join(df[''Text_Search''])), "Yes", "No")

我想要的输出如下所示：

          Date           Description   Amount  AutoNumber  \0    3/17/2015  WW120 TFR?FR xxx8690   140.00       49246   2    3/13/2015  JX154 TFR?FR xxx8690   150.00       49246   5     3/6/2015   CANSEL SURVEY E PAY  1182.08       49246   9     3/2/2015  UE200 TFR?FR xxx8690   180.00       49246   10   2/27/2015  JH401 TFR?FR xxx8690   400.00       49246   11   2/27/2015   CANSEL SURVEY E PAY   555.62       49246   12   2/25/2015  HU204 TFR?FR xxx8690   200.00       49246   13   2/23/2015  UQ263 TFR?FR xxx8690   102.00       49246   14   2/23/2015  UT460 TFR?FR xxx8690   200.00       49246   15   2/20/2015   CANSEL SURVEY E PAY  1222.05       49246   17   2/17/2015  UO414 TFR?FR xxx8690   250.00       49246   19   2/11/2015  HI540 TFR?FR xxx8690   130.00       49246   20   2/11/2015  HQ010 TFR?FR xxx8690   177.00       49246   21   2/10/2015  WU455 TFR?FR xxx8690   200.00       49246   22    2/6/2015  JJ500 TFR?FR xxx8690   301.00       49246   23    2/6/2015   CANSEL SURVEY E PAY  1182.08       49246   24    2/5/2015  IR453 TFR?FR xxx8690   168.56       49246   26    2/2/2015  RQ574 TFR?FR xxx8690   500.00       49246   27    2/2/2015  UT022 TFR?FR xxx8690   850.00       49246   28  12/31/2014  HU521 TFR?FR xxx8690   950.17       49246   29  12/30/2014  WZ553 TFR?FR xxx8690   200.00       49246   32  12/29/2014  JW173 TFR?FR xxx8690   300.00       49246   33  12/24/2014   CANSEL SURVEY E PAY  1219.21       49246   34  12/24/2014   CANSEL SURVEY E PAY   434.84       49246   36  12/23/2014  WT002 TFR?FR xxx8690   160.00       49246                   Employer                  Text_Search Match  0   Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  2   Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  5   Cansel Survey Equipment  [Cansel, Survey, Equipment]    Yes 9   Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  10  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  11  Cansel Survey Equipment  [Cansel, Survey, Equipment]    Yes  12  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  13  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  14  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  15  Cansel Survey Equipment  [Cansel, Survey, Equipment]    Yes  17  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  19  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  20  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  21  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  22  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  23  Cansel Survey Equipment  [Cansel, Survey, Equipment]    Yes  24  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  26  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  27  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  28  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  29  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  32  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No  33  Cansel Survey Equipment  [Cansel, Survey, Equipment]    Yes  34  Cansel Survey Equipment  [Cansel, Survey, Equipment]    Yes  36  Cansel Survey Equipment  [Cansel, Survey, Equipment]    No

答案1

小编典典

这是使用个人的可读解决方案search_func：

def search_func(row):    matches = [test_value in row["Description"].lower()                for test_value in row["Text_Search"]]    if any(matches):        return "Yes"    else:        return "No"

然后按行应用此函数：

# create example datadf = pd.DataFrame({"Description": ["CANSEL SURVEY E PAY", "JX154 TFR?FR xxx8690"],                   "Employer": ["Cansel Survey Equipment", "Cansel Survey Equipment"]})print(df)    Description             Employer0   CANSEL SURVEY E PAY     Cansel Survey Equipment1   JX154 TFR?FR xxx8690    Cansel Survey Equipment# create text searches and match columndf["Text_Search"] = df["Employer"].str.lower().str.split()df["Match"] = df.apply(search_func, axis=1)# show resultprint(df)    Description             Employer                    Text_Search                     Match0   CANSEL SURVEY E PAY     Cansel Survey Equipment     [cansel, survey, equipment]     Yes1   JX154 TFR?FR xxx8690    Cansel Survey Equipment     [cansel, survey, equipment]     No

Python Pandas -- DataFrame

Python Pandas -- DataFrame

pandas.DataFrame

class  pandas. DataFrame (data=None, index=None, columns=None, dtype=None, copy=False)[source]

Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure

Parameters:

data : numpy ndarray (structured or homogeneous), dict, or DataFrame

Dict can contain Series, arrays, constants, or list-like objects

index : Index or array-like

Index to use for resulting frame. Will default to np.arange(n) if no indexing information part of input data and no index provided

columns : Index or array-like

Column labels to use for resulting frame. Will default to np.arange(n) if no column labels are provided

dtype : dtype, default None

Data type to force. Only a single dtype is allowed. If None, infer

copy : boolean, default False

Copy data from inputs. Only affects DataFrame / 2d ndarray input

See also

DataFrame.from_records
constructor from tuples, also record arrays
DataFrame.from_dict
from dicts of Series, arrays, or dicts
DataFrame.from_items
from sequence of (key, value) pairs

pandas.read_csv, pandas.read_table, pandas.read_clipboard

1. 先来个小菜

  基于dictionary创建

from pandas import Series, DataFrame
import pandas as pd  
import numpy as np
d = {''col1'':[1,2],''col2'':[3,4]}
df = pd.DataFrame(data=d)
print(df)
print(df.dtypes)
#   col1  col2
#0     1     3
#1     2     4
#col1    int64
#col2    int64
#dtype: object

基于Numy的ndarrary

df2 = pd.DataFrame(np.random.randint(low=0, high=10, size=(5, 5)),columns=[''a'', ''b'', ''c'', ''d'', ''e''])
print (df2)
#   a  b  c  d  e
#0  0  2  4  7  0
#1  6  7  3  4  1
#2  5  3  3  8  7
#3  0  9  4  3  4
#4  7  4  7  0  0

 

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