用熊猫将一列转置

发布于 2025-02-10 07:16:59 字数 3097 浏览 4 评论 0原文

我正在尝试学习大熊猫，并且很难实现一个简单的目标。

我有一个数据集，我想在多列中对列的行进行转动（检查IMG）。列“最大起飞重量和发电厂类型”具有12个不同的值，目标是实现该值成为“值”填充的列。保留其他列也很重要，在此过程的后面，我将消除其中一些列，但我想知道是否可以实现目标，我知道这行中的某些值会丢失，例如“坐标”。

我已经阅读了有关

df=df.pivot_table(index=['REF_DATE','GEO','DGUID','Airports','UOM','UOM_ID','SCALAR_FACTOR','SCALAR_ID','VECTOR','COORDINATE','STATUS','SYMBOL','TERMINATED','DECIMALS'],
                  columns='Maximum take-off weight and type of power plant',
                  values ='VALUE')

输出应该是这样的：

输入样本：

"REF_DATE","GEO","DGUID","Airports","Maximum take-off weight and type of power plant","UOM","UOM_ID","SCALAR_FACTOR","SCALAR_ID","VECTOR","COORDINATE","VALUE","STATUS","SYMBOL","TERMINATED","DECIMALS"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 2,000 kilograms and under","Number","223","units ","0","v41840825","1.1.1","3551","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 2,001 to 4,000 kilograms","Number","223","units ","0","v41840829","1.1.2","4702","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 4,001 to 5,670 kilograms","Number","223","units ","0","v41840830","1.1.3","4293","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 5,671 to 9,000 kilograms","Number","223","units ","0","v41840831","1.1.4","1498","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 9,001 to 18,000 kilograms","Number","223","units ","0","v41840832","1.1.5","1704","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 18,001 to 35,000 kilograms","Number","223","units ","0","v41840833","1.1.6","1790","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 35,001 kilograms and over","Number","223","units ","0","v41840834","1.1.7","202","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, jet engines","Number","223","units ","0","v41840835","1.1.8","475","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, turbo-propellers","Number","223","units ","0","v41840836","1.1.9","9645","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, piston engines","Number","223","units ","0","v41840826","1.1.10","6398","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, helicopters","Number","223","units ","0","v41840827","1.1.11","1203","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, gliders","Number","223","units ","0","v41840828","1.1.12","19","","","","0"

原文

I'm trying to learn Pandas and I'm having difficulties to achieve a simple goal.

I have a dataset where I want to transpose the rows of a column in multiple column (check img).
The column "Maximum take-off weight and type of power plant" has 12 different values and the goal is to achive this values become columns filled with "VALUE".
It's also important to keep the other columns, later in the process I will eliminate some of them but I was wondering if it's possible to achieve the goal anyway, I know some of the values in this rows will be lost, like 'COORDINATE'.

I have read the documentation about pivot and pivot_table, but I haven't achieved anything. Since I want to keep all the columns and just transform 'Maximum take-off weight and type of power plant' values into columns, filled with values in 'VALUE' I thought I would need to add to 'index' all columns I want to keep.

df=df.pivot_table(index=['REF_DATE','GEO','DGUID','Airports','UOM','UOM_ID','SCALAR_FACTOR','SCALAR_ID','VECTOR','COORDINATE','STATUS','SYMBOL','TERMINATED','DECIMALS'],
                  columns='Maximum take-off weight and type of power plant',
                  values ='VALUE')

The output should be something like this:

Input sample:

"REF_DATE","GEO","DGUID","Airports","Maximum take-off weight and type of power plant","UOM","UOM_ID","SCALAR_FACTOR","SCALAR_ID","VECTOR","COORDINATE","VALUE","STATUS","SYMBOL","TERMINATED","DECIMALS"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 2,000 kilograms and under","Number","223","units ","0","v41840825","1.1.1","3551","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 2,001 to 4,000 kilograms","Number","223","units ","0","v41840829","1.1.2","4702","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 4,001 to 5,670 kilograms","Number","223","units ","0","v41840830","1.1.3","4293","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 5,671 to 9,000 kilograms","Number","223","units ","0","v41840831","1.1.4","1498","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 9,001 to 18,000 kilograms","Number","223","units ","0","v41840832","1.1.5","1704","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 18,001 to 35,000 kilograms","Number","223","units ","0","v41840833","1.1.6","1790","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 35,001 kilograms and over","Number","223","units ","0","v41840834","1.1.7","202","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, jet engines","Number","223","units ","0","v41840835","1.1.8","475","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, turbo-propellers","Number","223","units ","0","v41840836","1.1.9","9645","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, piston engines","Number","223","units ","0","v41840826","1.1.10","6398","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, helicopters","Number","223","units ","0","v41840827","1.1.11","1203","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, gliders","Number","223","units ","0","v41840828","1.1.12","19","","","","0"

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╭ゆ眷念 2025-02-17 07:16:59

基于问题中的示例，看来您只需要value在第一行中的条目匹配每个唯一最大起飞重量和发电厂类型条目。

这是一种完成您要求的方法（为简单起见，我将列名缩短为最大权重和类型，减少了列的数量和使用的任意值）：

x = df.drop_duplicates('Max weight and type')
y = x[['Max weight and type', 'Value']].set_index('Max weight and type').T.reset_index(drop=True)
y.columns.name=None
cols = list(x.columns)
iWt, iVal = cols.index('Max weight and type'), cols.index('Value')
cols = cols[:iWt] + list(y.columns) + cols[iWt + 1:iVal] + cols[iVal + 1:]
res = x.loc[0,:].drop(columns=['Max weight and type', 'Value']).to_frame().T.reindex(
    columns=cols).assign(**{k:v[0] for k, v in y.to_dict().items()})

示例输入：

              Airports Max weight and type  Value COORDINATES
0  Total, all airports                  x1     50       1.1.1
1  Total, all airports                  x2    100       1.1.2
2  Total, all airports                  x3    150       1.1.3
3  Total, all airports                  x4    200       1.1.4
4  Total, all airports                  x1     55       1.1.5
5  Total, all airports                  x2    105       1.1.6
6  Total, all airports                  x3    155       1.1.7

输出：输出：

              Airports  x1   x2   x3   x4 COORDINATES
0  Total, all airports  50  100  150  200       1.1.1

说明：

使用消除除最大权重和类型列中每个唯一值的第一行以外的所有行，
创建一个新的数据框type and唯一的行包含value在原始dataFrame的第一行中匹配每个唯一最大ravey and Type
输入列表列表列表列表，该列表替换最大重量和类型及其唯一值，并从原始数据框架的列标签中消除value
删除要替换的列（最大权重和类型， value），使用reindex（）添加最大权重和类型值作为列标记（最初使用NAN值），并使用nistion（）覆盖这些新列中的NAN值，并使用原始DataFrame的相应value条目。

Based on the example in your question, it looks like you only need the Value entry in the first row matching each unique Maximum take-off weight and type of power plant entry.

Here is a way to do what you've asked (for simplicity, I have shortened the column name to Max weight and type, reduced the number of columns and used arbitrary values):

x = df.drop_duplicates('Max weight and type')
y = x[['Max weight and type', 'Value']].set_index('Max weight and type').T.reset_index(drop=True)
y.columns.name=None
cols = list(x.columns)
iWt, iVal = cols.index('Max weight and type'), cols.index('Value')
cols = cols[:iWt] + list(y.columns) + cols[iWt + 1:iVal] + cols[iVal + 1:]
res = x.loc[0,:].drop(columns=['Max weight and type', 'Value']).to_frame().T.reindex(
    columns=cols).assign(**{k:v[0] for k, v in y.to_dict().items()})

Sample input:

              Airports Max weight and type  Value COORDINATES
0  Total, all airports                  x1     50       1.1.1
1  Total, all airports                  x2    100       1.1.2
2  Total, all airports                  x3    150       1.1.3
3  Total, all airports                  x4    200       1.1.4
4  Total, all airports                  x1     55       1.1.5
5  Total, all airports                  x2    105       1.1.6
6  Total, all airports                  x3    155       1.1.7

Output:

              Airports  x1   x2   x3   x4 COORDINATES
0  Total, all airports  50  100  150  200       1.1.1

Explanation:

Use drop_duplicates() to eliminate all but the first row for each unique value in the Max weight and type column
Create a new DataFrame whose column labels are the unique values from Max weight and type and whose only row contains the Value entry in the first row of the original dataframe matching each unique Max weight and type entry
Make a column label list which replaces Max weight and type with its unique values and eliminates Value from the original dataframe's column labels
Drop the columns to be replaced (Max weight and type, Value), use reindex() to add the Max weight and type values as column labels (with NaN values initially), and use assign() to overwrite the NaN values in these new columns with the corresponding Value entries from the original dataframe.

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合约呢 2025-02-17 07:16:59

如果您要保留原始DF，并且只需在两个不同现有列中添加一些带有名称和值的列，则可以用说明原始值仅填充新列的第一行。问候...

import pandas as pd

#   sample dictionary
data = {'A': [11,47,33],
        'B': ['max_x','max_y','max_z'],
        'C': [77,56,99]
        }
#   sample df
df = pd.DataFrame(data, columns = ['A', 'B', 'C'])

#   Create two lists of columns containing names and values for new columns
b_list = list(df['B'])
c_list = list(df['C'])

# To keep the whole df as it is and add some new columns get the length of the series in the column (number of rows)
list_length = len(b_list)

#   for new column create list populated with zeros so you would have a list of the size of the df series
i = 0
new_col_list = []
for i in range(list_length):
    new_col_list.append(0)

#   insert columns
for i in range(list_length):
    # put the value from your original df in the first element of the new_col_list, all others will be zeros
    new_col_list[0] = c_list[i]     
    # create and execute the command to insert a new column at position where you want (the end in this case)
    cmd = 'df.insert(' + str(len(b_list)  + i) + ', "' + str(b_list[i]) + '", ' + str(new_col_list) +', True)'
    exec(cmd)
    i += 1
print(df)
#
#   Original df
#
#           A      B   C
#       0  11  max_x  77
#       1  47  max_y  56
#       2  33  max_z  99
#
#
#   Insert commands generated in this script are:
#
#   df.insert(3, "max_x", [77, 0, 0], True)
#   df.insert(4, "max_y", [56, 0, 0], True)
#   df.insert(5, "max_z", [99, 0, 0], True)
#
#   ---------------------------------------------
#   R e s u l t :
#
#           A      B   C  max_x  max_y  max_z
#       0  11  max_x  77     77     56     99
#       1  47  max_y  56      0      0      0
#       2  33  max_z  99      0      0      0

In case you want to keep the original df and just add some columns with names and values from two different existing columns you can try this with remark that the original values populate just the first row of new columns. Regards...

import pandas as pd

#   sample dictionary
data = {'A': [11,47,33],
        'B': ['max_x','max_y','max_z'],
        'C': [77,56,99]
        }
#   sample df
df = pd.DataFrame(data, columns = ['A', 'B', 'C'])

#   Create two lists of columns containing names and values for new columns
b_list = list(df['B'])
c_list = list(df['C'])

# To keep the whole df as it is and add some new columns get the length of the series in the column (number of rows)
list_length = len(b_list)

#   for new column create list populated with zeros so you would have a list of the size of the df series
i = 0
new_col_list = []
for i in range(list_length):
    new_col_list.append(0)

#   insert columns
for i in range(list_length):
    # put the value from your original df in the first element of the new_col_list, all others will be zeros
    new_col_list[0] = c_list[i]     
    # create and execute the command to insert a new column at position where you want (the end in this case)
    cmd = 'df.insert(' + str(len(b_list)  + i) + ', "' + str(b_list[i]) + '", ' + str(new_col_list) +', True)'
    exec(cmd)
    i += 1
print(df)
#
#   Original df
#
#           A      B   C
#       0  11  max_x  77
#       1  47  max_y  56
#       2  33  max_z  99
#
#
#   Insert commands generated in this script are:
#
#   df.insert(3, "max_x", [77, 0, 0], True)
#   df.insert(4, "max_y", [56, 0, 0], True)
#   df.insert(5, "max_z", [99, 0, 0], True)
#
#   ---------------------------------------------
#   R e s u l t :
#
#           A      B   C  max_x  max_y  max_z
#       0  11  max_x  77     77     56     99
#       1  47  max_y  56      0      0      0
#       2  33  max_z  99      0      0      0

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驱逐舰岛风号 2025-02-17 07:16:59

您可以尝试：

import pandas as pd
from io import StringIO

#pd.set_option('display.max_colwidth', 10)
#pd.set_option('display.max_columns', None) 

df = """
"REF_DATE","GEO","DGUID","Airports","Maximum take-off weight and type of power plant","UOM","UOM_ID","SCALAR_FACTOR","SCALAR_ID","VECTOR","COORDINATE","VALUE","STATUS","SYMBOL","TERMINATED","DECIMALS"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 2,000 kilograms and under","Number","223","units ","0","v41840825","1.1.1","3551","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 2,001 to 4,000 kilograms","Number","223","units ","0","v41840829","1.1.2","4702","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 4,001 to 5,670 kilograms","Number","223","units ","0","v41840830","1.1.3","4293","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 5,671 to 9,000 kilograms","Number","223","units ","0","v41840831","1.1.4","1498","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 9,001 to 18,000 kilograms","Number","223","units ","0","v41840832","1.1.5","1704","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 18,001 to 35,000 kilograms","Number","223","units ","0","v41840833","1.1.6","1790","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 35,001 kilograms and over","Number","223","units ","0","v41840834","1.1.7","202","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, jet engines","Number","223","units ","0","v41840835","1.1.8","475","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, turbo-propellers","Number","223","units ","0","v41840836","1.1.9","9645","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, piston engines","Number","223","units ","0","v41840826","1.1.10","6398","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, helicopters","Number","223","units ","0","v41840827","1.1.11","1203","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, gliders","Number","223","units ","0","v41840828","1.1.12","19","","","","0"
"1997-01","Canada","2016A000011125","Test2","Power plant, piston engines","Number","223","units ","0","v41840826","1.1.10","10000","","","","0"
"1997-01","Canada","2016A000011125","Test2","Power plant, helicopters","Number","223","units ","0","v41840827","1.1.11","10001","","","","0"
"1997-01","Canada","2016A000011125","Test2","Power plant, gliders","Number","223","units ","0","v41840828","1.1.12","10002","","","","0"


"""
df = pd.read_csv(StringIO(df.strip()), sep=',')



#create a pivot to have one line per DGUID
df1=pd.pivot_table(df, values='VALUE', index='DGUID', columns='Maximum take-off weight and type of power plant').fillna(0).reset_index()

#Merge pivot with intial df
df=df.merge(df1,on='DGUID')
print("----------------------")
print('with All lines:')
print(df)

#if only first line of each DGUID is intereting:
df.drop_duplicates('DGUID',inplace=True)
print("----------------------")
print('Duplicates on DGUID removed:')
print(df)

结果：

----------------------
with All lines:
   REF_DATE     GEO           DGUID             Airports  ... Power plant, helicopters Power plant, jet engines  Power plant, piston engines Power plant, turbo-propellers
0   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
1   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
2   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
3   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
4   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
5   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
6   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
7   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
8   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
9   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
10  1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
11  1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
12  1997-01  Canada  2016A000011125                Test2  ...                  10001.0                      0.0                      10000.0                           0.0
13  1997-01  Canada  2016A000011125                Test2  ...                  10001.0                      0.0                      10000.0                           0.0
14  1997-01  Canada  2016A000011125                Test2  ...                  10001.0                      0.0                      10000.0                           0.0

[15 rows x 28 columns]
----------------------
Duplicates on DGUID removed:
   REF_DATE     GEO           DGUID             Airports  ... Power plant, helicopters Power plant, jet engines  Power plant, piston engines Power plant, turbo-propellers
0   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
12  1997-01  Canada  2016A000011125                Test2  ...                  10001.0                      0.0                      10000.0                           0.0

[2 rows x 28 columns]

you can try:

import pandas as pd
from io import StringIO

#pd.set_option('display.max_colwidth', 10)
#pd.set_option('display.max_columns', None) 

df = """
"REF_DATE","GEO","DGUID","Airports","Maximum take-off weight and type of power plant","UOM","UOM_ID","SCALAR_FACTOR","SCALAR_ID","VECTOR","COORDINATE","VALUE","STATUS","SYMBOL","TERMINATED","DECIMALS"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 2,000 kilograms and under","Number","223","units ","0","v41840825","1.1.1","3551","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 2,001 to 4,000 kilograms","Number","223","units ","0","v41840829","1.1.2","4702","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 4,001 to 5,670 kilograms","Number","223","units ","0","v41840830","1.1.3","4293","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 5,671 to 9,000 kilograms","Number","223","units ","0","v41840831","1.1.4","1498","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 9,001 to 18,000 kilograms","Number","223","units ","0","v41840832","1.1.5","1704","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 18,001 to 35,000 kilograms","Number","223","units ","0","v41840833","1.1.6","1790","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Maximum take-off weight, 35,001 kilograms and over","Number","223","units ","0","v41840834","1.1.7","202","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, jet engines","Number","223","units ","0","v41840835","1.1.8","475","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, turbo-propellers","Number","223","units ","0","v41840836","1.1.9","9645","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, piston engines","Number","223","units ","0","v41840826","1.1.10","6398","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, helicopters","Number","223","units ","0","v41840827","1.1.11","1203","","","","0"
"1997-01","Canada","2016A000011124","Total, all airports","Power plant, gliders","Number","223","units ","0","v41840828","1.1.12","19","","","","0"
"1997-01","Canada","2016A000011125","Test2","Power plant, piston engines","Number","223","units ","0","v41840826","1.1.10","10000","","","","0"
"1997-01","Canada","2016A000011125","Test2","Power plant, helicopters","Number","223","units ","0","v41840827","1.1.11","10001","","","","0"
"1997-01","Canada","2016A000011125","Test2","Power plant, gliders","Number","223","units ","0","v41840828","1.1.12","10002","","","","0"


"""
df = pd.read_csv(StringIO(df.strip()), sep=',')



#create a pivot to have one line per DGUID
df1=pd.pivot_table(df, values='VALUE', index='DGUID', columns='Maximum take-off weight and type of power plant').fillna(0).reset_index()

#Merge pivot with intial df
df=df.merge(df1,on='DGUID')
print("----------------------")
print('with All lines:')
print(df)

#if only first line of each DGUID is intereting:
df.drop_duplicates('DGUID',inplace=True)
print("----------------------")
print('Duplicates on DGUID removed:')
print(df)

Result:

----------------------
with All lines:
   REF_DATE     GEO           DGUID             Airports  ... Power plant, helicopters Power plant, jet engines  Power plant, piston engines Power plant, turbo-propellers
0   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
1   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
2   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
3   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
4   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
5   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
6   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
7   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
8   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
9   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
10  1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
11  1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
12  1997-01  Canada  2016A000011125                Test2  ...                  10001.0                      0.0                      10000.0                           0.0
13  1997-01  Canada  2016A000011125                Test2  ...                  10001.0                      0.0                      10000.0                           0.0
14  1997-01  Canada  2016A000011125                Test2  ...                  10001.0                      0.0                      10000.0                           0.0

[15 rows x 28 columns]
----------------------
Duplicates on DGUID removed:
   REF_DATE     GEO           DGUID             Airports  ... Power plant, helicopters Power plant, jet engines  Power plant, piston engines Power plant, turbo-propellers
0   1997-01  Canada  2016A000011124  Total, all airports  ...                   1203.0                    475.0                       6398.0                        9645.0
12  1997-01  Canada  2016A000011125                Test2  ...                  10001.0                      0.0                      10000.0                           0.0

[2 rows x 28 columns]

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