Plotly: Dodge overlapping points on scatterplot categorical axis - python

I am trying to use plotly to compare the coefficents of regression models using error bars for the confidence intervals. I used the following code to plot it, using the variable as a categorical y axis in a scatter plot. The problem is that the points are overlapping, and I'd like to dodge them like happens in bar charts when you set barmode='group'. If I had a numerical axis I could manually dodge them, but I can't do that.
fig = px.scatter(
df, y='index', x='coef', text='label', color='model',
error_x_minus='lerr', error_x='uerr',
hover_data=['coef', 'pvalue', 'lower', 'upper']
)
fig.update_traces(textposition='top center')
fig.update_yaxes(autorange="reversed")
Using facets I get almost the result I want, but some of the labels goes off-plot and are not visible:
fig = px.scatter(
df, y='model', x='coef', text='label', color='model',
facet_row='index',
error_x_minus='lerr', error_x='uerr',
hover_data=['coef', 'pvalue', 'lower', 'upper']
)
fig.update_traces(textposition='top center')
fig.update_yaxes(visible=False)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
Somebody has any idea or workaround for either dodging points in the first case or displaying labels in the second case?
Thanks in advance.
PS: Here's the random fake dataframe I made to generate the plots:
df = pd.DataFrame({'coef': {0: 1.0018729737113143,
1: 0.9408864645423858,
2: 0.29796556981484884,
3: -0.6844053575764955,
4: -0.13689631932690113,
5: 0.1473096200402363,
6: 0.9564712505670716,
7: 0.956099003887811,
8: 0.33319108930207175,
9: -0.7022778825729681,
10: -0.1773916842612131,
11: 0.09485417304851751},
'index': {0: 'const',
1: 'x1',
2: 'x2',
3: 'x3',
4: 'x4',
5: 'x5',
6: 'const',
7: 'x1',
8: 'x2',
9: 'x3',
10: 'x4',
11: 'x5'},
'label': {0: '1.002***',
1: '0.941***',
2: '0.298***',
3: '-0.684***',
4: '-0.137',
5: '0.147',
6: '0.956***',
7: '0.956***',
8: '0.333***',
9: '-0.702***',
10: '-0.177',
11: '0.095'},
'lerr': {0: 0.19788416996400904,
1: 0.19972987383410545,
2: 0.0606849959013587,
3: 0.1772734289533593,
4: 0.1988122854078155,
5: 0.21870366703236832,
6: 0.2734783191688098,
7: 0.2760291042678362,
8: 0.08386739920069491,
9: 0.2449940255063039,
10: 0.27476098595116555,
11: 0.3022511162310027},
'lower': {0: 0.8039888037473053,
1: 0.7411565907082803,
2: 0.23728057391349014,
3: -0.8616787865298547,
4: -0.33570860473471664,
5: -0.07139404699213203,
6: 0.6829929313982618,
7: 0.6800698996199748,
8: 0.24932369010137684,
9: -0.947271908079272,
10: -0.45215267021237865,
11: -0.2073969431824852},
'model': {0: 'OLS',
1: 'OLS',
2: 'OLS',
3: 'OLS',
4: 'OLS',
5: 'OLS',
6: 'QuantReg',
7: 'QuantReg',
8: 'QuantReg',
9: 'QuantReg',
10: 'QuantReg',
11: 'QuantReg'},
'pvalue': {0: 1.4211692095019375e-16,
1: 4.3583690618389965e-15,
2: 6.278403727223468e-16,
3: 1.596372747840846e-11,
4: 0.17483151363955116,
5: 0.18433051296752084,
6: 4.877385844808361e-10,
7: 6.665860891682504e-10,
8: 5.476882838731488e-12,
9: 1.4240852942202845e-07,
10: 0.20303143985022934,
11: 0.5347222575215599},
'uerr': {0: 0.19788416996400904,
1: 0.19972987383410556,
2: 0.06068499590135873,
3: 0.1772734289533593,
4: 0.19881228540781554,
5: 0.21870366703236832,
6: 0.27347831916880994,
7: 0.2760291042678362,
8: 0.08386739920069491,
9: 0.2449940255063039,
10: 0.27476098595116555,
11: 0.3022511162310027},
'upper': {0: 1.1997571436753234,
1: 1.1406163383764913,
2: 0.35865056571620757,
3: -0.5071319286231362,
4: 0.0619159660809144,
5: 0.3660132870726046,
6: 1.2299495697358815,
7: 1.2321281081556472,
8: 0.41705848850276667,
9: -0.4572838570666642,
10: 0.09736930168995245,
11: 0.3971052892795202}})


You were very close to a working solution with your second attempt. Just make more room for your labels with:
height=600, width=800
And then place the labels for the traces named 'OLS' within the boundaries of each subplot with:
fig.for_each_trace(lambda t: t.update(textposition='bottom center') if t.name == 'OLS' else ())
Plot:
Complete code:
import plotly.express as px
import pandas as pd
df = pd.DataFrame({'coef': {0: 1.0018729737113143,
1: 0.9408864645423858,
2: 0.29796556981484884,
3: -0.6844053575764955,
4: -0.13689631932690113,
5: 0.1473096200402363,
6: 0.9564712505670716,
7: 0.956099003887811,
8: 0.33319108930207175,
9: -0.7022778825729681,
10: -0.1773916842612131,
11: 0.09485417304851751},
'index': {0: 'const',
1: 'x1',
2: 'x2',
3: 'x3',
4: 'x4',
5: 'x5',
6: 'const',
7: 'x1',
8: 'x2',
9: 'x3',
10: 'x4',
11: 'x5'},
'label': {0: '1.002***',
1: '0.941***',
2: '0.298***',
3: '-0.684***',
4: '-0.137',
5: '0.147',
6: '0.956***',
7: '0.956***',
8: '0.333***',
9: '-0.702***',
10: '-0.177',
11: '0.095'},
'lerr': {0: 0.19788416996400904,
1: 0.19972987383410545,
2: 0.0606849959013587,
3: 0.1772734289533593,
4: 0.1988122854078155,
5: 0.21870366703236832,
6: 0.2734783191688098,
7: 0.2760291042678362,
8: 0.08386739920069491,
9: 0.2449940255063039,
10: 0.27476098595116555,
11: 0.3022511162310027},
'lower': {0: 0.8039888037473053,
1: 0.7411565907082803,
2: 0.23728057391349014,
3: -0.8616787865298547,
4: -0.33570860473471664,
5: -0.07139404699213203,
6: 0.6829929313982618,
7: 0.6800698996199748,
8: 0.24932369010137684,
9: -0.947271908079272,
10: -0.45215267021237865,
11: -0.2073969431824852},
'model': {0: 'OLS',
1: 'OLS',
2: 'OLS',
3: 'OLS',
4: 'OLS',
5: 'OLS',
6: 'QuantReg',
7: 'QuantReg',
8: 'QuantReg',
9: 'QuantReg',
10: 'QuantReg',
11: 'QuantReg'},
'pvalue': {0: 1.4211692095019375e-16,
1: 4.3583690618389965e-15,
2: 6.278403727223468e-16,
3: 1.596372747840846e-11,
4: 0.17483151363955116,
5: 0.18433051296752084,
6: 4.877385844808361e-10,
7: 6.665860891682504e-10,
8: 5.476882838731488e-12,
9: 1.4240852942202845e-07,
10: 0.20303143985022934,
11: 0.5347222575215599},
'uerr': {0: 0.19788416996400904,
1: 0.19972987383410556,
2: 0.06068499590135873,
3: 0.1772734289533593,
4: 0.19881228540781554,
5: 0.21870366703236832,
6: 0.27347831916880994,
7: 0.2760291042678362,
8: 0.08386739920069491,
9: 0.2449940255063039,
10: 0.27476098595116555,
11: 0.3022511162310027},
'upper': {0: 1.1997571436753234,
1: 1.1406163383764913,
2: 0.35865056571620757,
3: -0.5071319286231362,
4: 0.0619159660809144,
5: 0.3660132870726046,
6: 1.2299495697358815,
7: 1.2321281081556472,
8: 0.41705848850276667,
9: -0.4572838570666642,
10: 0.09736930168995245,
11: 0.3971052892795202}})
fig = px.scatter(
df, y='model', x='coef', text='label', color='model',
facet_row='index',
error_x_minus='lerr', error_x='uerr',
hover_data=['coef', 'pvalue', 'lower', 'upper'],
height=600, width=800,
)
fig.update_traces(textposition='top center')
fig.update_yaxes(visible=False)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.for_each_trace(lambda t: t.update(textposition='bottom center') if t.name == 'OLS' else ())
fig.show()

Related

how to add text on each rectangle in collection using matplotlib?

I am using matplotlib for plotting and convenient visualization of some graphs in xy coordinates.
I need to highlight some regions - and I use rectangles for this.
But I am interested to add some text upon each rectangle - to be able to distinguish those regions. How to do it using patches because I have a lot of objects in a plot?
Here is the code I use to plot rectangles:
# sample data for rectangles visualization
windows_df = pd.DataFrame( {'window_index_num': {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}, 'left_pulse_num': {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}, 'right_pulse_num': {0: 2, 1: 3, 2: 4, 3: 5, 4: 6, 5: 7, 6: 8, 7: 9, 8: 10, 9: 11}, 'idx_of_left_pulse': {0: 0, 1: 4036, 2: 4080, 3: 4107, 4: 4368, 5: 4491, 6: 4529, 7: 4624, 8: 4626, 9: 4639}, 'idx_of_right_pulse': {0: 4080, 1: 4107, 2: 4368, 3: 4491, 4: 4529, 5: 4624, 6: 4626, 7: 4639, 8: 4679, 9: 4781}, 'left_pulse_pos_in_E': {0: 10.002042118364418, 1: 40.29395464818188, 2: 41.19356816747343, 3: 41.76060061888303, 4: 47.90221207147802, 5: 51.27679395217831, 6: 52.39165780468267, 7: 55.37561818764979, 8: 55.47294132608167, 9: 55.99635666692289}, 'right_pulse_pos_in_E': {0: 41.19356816747343, 1: 41.76060061888303, 2: 47.90221207147802, 3: 51.27679395217831, 4: 52.39165780468267, 5: 55.37561818764979, 6: 55.47294132608167, 7: 55.99635666692289, 8: 57.33777021469516, 9: 60.984834434908144}, 'idx_window_left_border': {0: 0, 1: 3990, 2: 4058, 3: 4093, 4: 4237, 5: 4429, 6: 4510, 7: 4576, 8: 4625, 9: 4632}, 'idx_window_right_border': {0: 4094, 1: 4238, 2: 4430, 3: 4510, 4: 4577, 5: 4625, 6: 4633, 7: 4659, 8: 4730, 9: 4792}, 'left_win_pos_in_E': {0: 10.002042118364418, 1: 39.38459790393702, 2: 40.74003692229216, 3: 41.46513255508269, 4: 44.66179219947279, 5: 49.53272998148, 6: 51.82972979173252, 7: 53.82159300113625, 8: 55.40803086073492, 9: 55.76645477820397}, 'right_win_pos_in_E': {0: 41.48613320837913, 1: 44.6852679849016, 2: 49.56014983071213, 3: 51.82972979173252, 4: 53.85265044341121, 5: 55.40803086073492, 6: 55.79921126600202, 7: 56.66110947958804, 8: 59.119140585251095, 9: 61.39880967219205}, 'window_width': {0: 4095, 1: 249, 2: 373, 3: 418, 4: 341, 5: 197, 6: 124, 7: 84, 8: 106, 9: 161}, 'window_width_in_E': {0: 31.48409109001471, 1: 5.300670080964579, 2: 8.820112908419965, 3: 10.364597236649828, 4: 9.190858243938415, 5: 5.875300879254915, 6: 3.9694814742695, 7: 2.8395164784517917, 8: 3.7111097245161773, 9: 5.632354893988079}, 'sum_pulses_duration_in_E': {0: 0.5157099691135514, 1: 0.5408987779694527, 2: 0.6869248977656355, 3: 0.7304908951030242, 4: 0.7269657511683718, 5: 0.537271616198268, 6: 0.7609034761658222, 7: 0.6178183490930067, 8: 0.8269277926972265, 9: 0.5591109437337494}, 'sum_pulse_sq': {0: 3.7944375922206044, 1: 3.8756992116858715, 2: 2.9661915477796663, 3: 3.070559830941317, 4: 3.0597037730539385, 5: 10.2020204659669, 6: 45.77535573608872, 7: 45.87630607524008, 8: 39.10335270063814, 9: 3.437205923490125}, 'pulse_to_window_rate': {0: 0.01638001769335214, 1: 0.10204347180781788, 2: 0.07788164447530765, 3: 0.0704794290047244, 4: 0.0790966122938326, 5: 0.09144580460471718, 6: 0.1916883807363909, 7: 0.2175787158769594, 8: 0.22282493757444324, 9: 0.09926770493999569}, 'max_height_in_window': {0: 20.815950580921104, 1: 20.815950580921104, 2: 5.324888970962656, 3: 5.324888970962656, 4: 5.14075603114903, 5: 86.81228155905252, 6: 110.06755904473022, 7: 110.06755904473022, 8: 110.06755904473022, 9: 14.735092268739246}, 'min_height_in_window': {0: -0.011928180619527797, 1: 1.6172637244080776, 2: 1.6172637244080776, 3: 0.8658702248969847, 4: 0.8658702248969847, 5: 0.8658702248969847, 6: 1.8476229914953515, 7: 2.918666252051556, 8: 3.2397786967451707, 9: 2.4893555139463266}, 'windows_sq': {0: 655.3712842149647, 1: 110.33848645112575, 2: 46.96612194869083, 3: 55.19032951390669, 4: 47.24795994896218, 5: 510.0482741740266, 6: 436.911136546121, 7: 312.538647650477, 8: 408.4727887246568, 9: 82.9932690531994}} )
fig_w, axs_w = plt.subplots()
#theoretical cross-section
#axs_w.plot(df_wo_NANS['E'], df_wo_NANS['theo_cs'], marker = "o", markersize = 1, linewidth = 1.0, alpha=0.6, color = 'green', label = 'Theo Cross Section')
axs_w.grid(color = 'grey', linestyle = '--', linewidth = 0.2)
#windows rectangular
from matplotlib.collections import PatchCollection
from matplotlib.patches import Rectangle
boxes = []
for index,row in windows_df.iterrows():
current_rect_left_corner = (row['left_win_pos_in_E'], row['min_height_in_window'])
current_w = row['window_width_in_E']
current_h = row['max_height_in_window']-row['min_height_in_window']
boxes.append(Rectangle(current_rect_left_corner, current_w, current_h))
left = row['left_win_pos_in_E']
right = row['right_win_pos_in_E']
bottom = row['min_height_in_window']
top = row['max_height_in_window']
#mark of the start of the current window
axs_w.text(
left, #left corner, #0.5*(left+right), #middle of the rectangle
top, #top
str(index),
horizontalalignment='center',
verticalalignment='center',
fontsize=5
)
#mark of the end of the current window
axs_w.text(
right, #right corner, #0.5*(left+right), #middle of the rectangle
top+0.5*bottom, #top
str(index)+'e',
horizontalalignment='center',
verticalalignment='center',
fontsize=5
)
pc = PatchCollection(boxes, facecolor='y', alpha=0.2, edgecolor='black')
axs_w.add_collection(pc)
Added text marks using cycle but is it possible to do it using patch and collections to make more efficient code?

How to substract two dates based on filter of two other columns

I am new in Python and I am struggling to reshape my dataFrame.
For a particular client (contact_id), I want to add an new date column that actually substracts the DTHR_OPERATION date for a 'TYPE_OPER_VALIDATION = 3' minus the DTHR_OPERATION date for a 'TYPE_OPER_VALIDATION = 1'.
If the 'TYPE_OPER_VALIDATION' is equal to 3 and that there is less than a hour difference between those two dates, I want to add a string such as 'connection' for example in the new column.
I have an issue "python Series' object has no attribute 'total_seconds" when I try to compare if the time difference is indeed minus or equal to an hour. I tried many solutions I found on Internet but I always seem to have a data type issue.
Here is my code snippet:
df_oper_one = merged_table.loc[(merged_table['TYPE_OPER_VALIDATION']==1),['contact_id','TYPE_OPER_VALIDATION','DTHR_OPERATION']]
df_oper_three = merged_table.loc[(merged_table['TYPE_OPER_VALIDATION']==3),['contact_id','TYPE_OPER_VALIDATION','DTHR_OPERATION']]
connection = []
for row in merged_table['contact_id']:
if (df_validation.loc[(df_validation['TYPE_OPER_VALIDATION']==3)]) & ((pd.to_datetime(df_oper_three['DTHR_OPERATION'],format='%Y-%m-%d %H:%M:%S') - pd.to_datetime(df_oper_one['DTHR_OPERATION'],format='%Y-%m-%d %H:%M:%S').total_seconds()) <= 3600): connection.append('connection')
# if diff_date.total_seconds() <= 3600: connection.append('connection')
else: connection.append('null')
merged_table['connection'] = pd.Series(connection)

Hello Nicolas and welcome to Stack Overflow. Please remember to always include sample data to reproduce your issue. Here is sample data to reproduce part of your dataframe:
df = pd.DataFrame({'Id contact':['cf2e79bc-8cac-ec11-9840-000d3ab078e6']*12+['865c5edf-c7ac-ec11-9840-000d3ab078e6']*10,
'DTHR OPERATION':['11/10/2022 07:07', '11/10/2022 07:29', '11/10/2022 15:47', '11/10/2022 16:22', '11/10/2022 16:44', '11/10/2022 18:06', '12/10/2022 07:11', '12/10/2022 07:25', '12/10/2022 17:21', '12/10/2022 18:04', '13/10/2022 07:09', '13/10/2022 18:36', '14/09/2022 17:59', '15/09/2022 09:34', '15/09/2022 19:17', '16/09/2022 08:31', '16/09/2022 19:18', '17/09/2022 06:41', '17/09/2022 11:19', '17/09/2022 15:48', '17/09/2022 16:13', '17/09/2022 17:07'],
'lastname':['BOUALAMI']*12+['VERVOORT']*10,
'TYPE_OPER_VALIDATION':[1, 3, 1, 3, 3, 3, 1, 3, 1, 3, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3]})
df['DTHR OPERATION'] = pd.to_datetime(df['DTHR OPERATION'])
I would recommend creating a new table to more easily accomplish your task:
df2 = pd.merge(df[['Id contact', 'DTHR OPERATION']][df['TYPE_OPER_VALIDATION']==3], df[['Id contact', 'DTHR OPERATION']][df['TYPE_OPER_VALIDATION']==1], on='Id contact', suffixes=('_type3','_type1'))
Then find the time difference:
df2['seconds'] = (df2['DTHR OPERATION_type3']-df2['DTHR OPERATION_type1']).dt.total_seconds()
Finally, flag connections of an hour or less:
df2['connection'] = np.where(df2['seconds']<=3600, 'yes', 'no')
Hope this helps!

sure, here is the information you are looking for :
df_contact = pd.DataFrame{'contact_id': {0: '865C5EDF-C7AC-EC11-9840', 1: '9C9690B1-F8AC-EC11', 2: '4DD27359-14AF-EC11-9840', 3: '0091373E-E7F4-4170-BCAC'}, 'birthdate': {0: Timestamp('2005-05-19
00:00:00'), 1: Timestamp('1982-01-28 00:00:00'), 2: Timestamp('1997-05-15 00:00:00'), 3: Timestamp('2005-03-22 00:00:00')}, 'fullname': {0: 'Laura VERVO', 1: 'Mélanie ALBE', 2: 'Eric VANO', 3: 'Jean Docq'}, 'lastname': {0: 'VERVO', 1: 'ALBE', 2: 'VANO', 3: 'Docq'}, 'age': {0: 17, 1: 40, 2: 25, 3: 17}}
df_validation = pd.dataframe{'validation_id': {0: 8263835881, 1: 8263841517, 2: 8263843376, 3: 8263843377, 4: 8263843381, 5: 8263843382, 6: 8263863088, 7: 8263863124, 8: 8263868113, 9: 8263868123}, 'LIBEL_LONG_PRODUIT_TITRE': {0: 'Mens NEXT 12-17', 1: 'Ann NEXT 25-64%B', 2: 'Ann EXPRESS CBLANCHE', 3: 'Multi 8 NEXT', 4: 'Ann EXPRESS 18-24', 5: 'SNCB+TEC NEXT ABO', 6: 'Ann EXPRESS 18-24', 7: 'Ann EXPRESS 12-17%B', 8: '1 jour EX Réfugié', 9: 'Ann EXPRESS 2564%B'}, 'DTHR_OPERATION':
{0: Timestamp('2022-10-01 00:02:02'), 1: Timestamp('2022-10-01 00:22:45'), 2: Timestamp('2022-10-01 00:02:45'), 3: Timestamp('2022-10-01 00:02:49'), 4: Timestamp('2022-10-01 00:07:03'), 5: Timestamp('2022-10-01 00:07:06'), 6: Timestamp('2022-10-01 00:07:40'), 7: Timestamp('2022-10-01 00:31:51'), 8: Timestamp('2022-10-01 00:03:33'), 9: Timestamp('2022-10-01 00:07:40')}, 'TYPE_OPER_VALIDATION': {0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 3, 7: 3, 8: 2, 9: 1}, 'NUM_SERIE_SUPPORT': {0: '2040121921', 1: '2035998914', 2: '2034456458', 3: '14988572652829627697', 4: '2035956003', 5: '2033613155', 6: '2040119429', 7: '2036114867', 8: '14988572650230713650', 9: '2040146199'}}
{'support_id': {0: '8D3A331D-3E86-EC11-93B0', 1: '44863926-3E86-EC11-93B0', 2: '45863926-3E86-EC11-93B0', 3: '46863926-3E86-EC11-93B0', 4: '47863926-3E86-EC11-93B0', 5: 'E3863926-3E86-EC11-93B0', 6: '56873926-3E86-EC11', 7: 'E3CE312C-3E86-EC11-93B0', 8: 'F3CE312C-3E86-EC11-93B0', 9: '3CCF312C-3E86-EC11-93B0'}, 'bd_linkedcustomer': {0: '15CCC384-C4AD-EC11', 1: '9D27061D-14AE-EC11-9840', 2: '74CAE68F-D4AC-EC11-9840', 3: '18F5FE1A-58AC-EC11-983F', 4: None, 5: '9FBDA103-2FAD-EC11-9840', 6: 'EEA1FB63-75AC-EC11-9840', 7: 'F150EC3D-0DAD-EC11-9840', 8: '111DE8C4-CAAC-EC11-9840', 9: None}, 'bd_supportserialnumber': {0: '44884259', 1: '2036010559', 2: '62863150', 3: '2034498160', 4: '62989611', 5: '2036094315', 6: '2033192919', 7: '2036051529', 8: '2036062236', 9: '2033889172'}}
df_support = pd.dataframe{'support_id': {0: '8D3A331D-3E86-EC11-93B0', 1: '44863926-3E86-EC11', 2: '45863926-3E86-EC11-93B0', 3: '46863926-3E86-EC11-93B0', 4: '47863926-3E86-EC11-93B0', 5: 'E3863926-3E86-EC11-93B0', 6: '56873926-3E86-EC11-93B0', 7: 'E3CE312C-3E86-EC11-93B0', 8: 'F3CE312C-3E86-EC11-93B0', 9: '3CCF312C-3E86-EC11-93B0'}, 'bd_linkedcustomer': {0: '15CCC384-C4AD-EC11-9840', 1: '9D27061D-14AE-EC11-9840', 2: '74CAE68F-D4AC-EC11-9840', 3: '18F5FE1A-58AC-EC11-983F', 4: None, 5: '9FBDA103-2FAD-EC11', 6: 'EEA1FB63-75AC-EC11-9840', 7: 'F150EC3D-0DAD-EC11-9840', 8: '111DE8C4-CAAC-EC11-9840', 9: None}, 'bd_supportserialnumber': {0: '44884259', 1: '2036010559', 2: '62863150', 3: '2034498160', 4: '62989611', 5: '2036094315', 6: '2033192919', 7: '2036051529', 8: '2036062236', 9: '2033889172'}}
df2 = pd.dataframe{'support_id': {0: '4BE73E8C-B8F9-EC11-BB3D', 1: '4BE73E8C-B8F9-EC11-BB3D', 2: '4BE73E8C-B8F9-EC11-BB3D', 3: '4BE73E8C-B8F9-EC11-BB3D', 4: '4BE73E8C-B8F9-EC11-BB3D', 5: '4BE73E8C-B8F9-EC11-BB3D', 6: '4BE73E8C-B8F9-EC11', 7: '4BE73E8C-B8F9-EC11-BB3D', 8: '4BE73E8C-B8F9-EC11-BB3D', 9: '4BE73E8C-B8F9-EC11-BB3D'}, 'bd_linkedcustomer': {0: '9C9690B1-F8AC-EC11-9840', 1: '9C9690B1-F8AC-EC11-9840', 2: '9C9690B1-F8AC-EC11-9840', 3: '9C9690B1-F8AC-EC11-9840', 4: '9C9690B1-F8AC-EC11-9840',
5: '9C9690B1-F8AC-EC11-9840', 6: '9C9690B1-F8AC-EC11-9840', 7: '9C9690B1-F8AC-EC11-9840', 8: '9C9690B1-F8AC-EC11-9840', 9: '9C9690B1-F8AC-EC11-9840'}, 'bd_supportserialnumber': {0: '2036002771', 1: '2036002771', 2: '2036002771', 3: '2036002771', 4: '2036002771', 5: '2036002771', 6: '2036002771', 7: '2036002771', 8: '2036002771', 9: '2036002771'}, 'contact_id': {0: '9C9690B1-F8AC-EC11-9840', 1: '9C9690B1-F8AC-EC11-9840', 2: '9C9690B1-F8AC-EC11-9840', 3: '9C9690B1-F8AC-EC11-9840', 4: '9C9690B1-F8AC-EC11-9840', 5: '9C9690B1-F8AC-EC11-9840', 6: '9C9690B1-F8AC-EC11-9840', 7: '9C9690B1-F8AC-EC11-9840', 8: '9C9690B1-F8AC-EC11-9840', 9: '9C9690B1-F8AC-EC11-9840'}, 'birthdate': {0: Timestamp('1982-01-28 00:00:00'), 1: Timestamp('1982-01-28 00:00:00'), 2: Timestamp('1982-01-28 00:00:00'), 3: Timestamp('1982-01-28 00:00:00'), 4: Timestamp('1982-01-28 00:00:00'), 5: Timestamp('1982-01-28 00:00:00'), 6: Timestamp('1982-01-28 00:00:00'), 7: Timestamp('1982-01-28 00:00:00'), 8: Timestamp('1982-01-28 00:00:00'), 9: Timestamp('1982-01-28 00:00:00')}, 'fullname': {0: 'Mélanie ALBE', 1: 'Mélanie ALBE', 2: 'Mélanie ALBE', 3: 'Mélanie ALBE', 4: 'Mélanie ALBE', 5: 'Mélanie ALBE', 6: 'Mélanie ALBE', 7: 'Mélanie ALBE', 8: 'Mélanie ALBE', 9: 'Mélanie ALBE'}, 'lastname': {0: 'ALBE', 1: 'ALBE', 2: 'ALBE', 3: 'ALBE', 4: 'ALBE', 5: 'ALBE', 6: 'ALBE', 7: 'ALBE', 8: 'ALBE', 9: 'ALBE'}, 'age': {0: 40, 1: 40, 2: 40, 3: 40, 4: 40, 5: 40, 6: 40, 7: 40, 8: 40, 9: 40}, 'validation_id': {0: 8264573419, 1: 8264574166, 2: 8264574345, 3: 8264676975, 4: 8265441741, 5: 8272463799, 6: 8272471694, 7: 8274368291, 8: 8274397366, 9: 8277077728}, 'LIBEL_LONG_PRODUIT_TITRE': {0: 'Ann NEXT 25-64', 1: 'Ann NEXT 25-64', 2: 'Ann NEXT 25-64', 3: 'Ann NEXT 25-64', 4: 'Ann NEXT 25-64', 5: 'Ann NEXT 25-64', 6: 'Ann NEXT 25-64', 7: 'Ann NEXT 25-64', 8: 'Ann NEXT 25-64', 9: 'Ann NEXT 25-64'}, 'DTHR_OPERATION': {0: Timestamp('2022-10-01 08:30:18'), 1: Timestamp('2022-10-01 12:23:34'), 2: Timestamp('2022-10-01 07:47:46'), 3: Timestamp('2022-10-01 13:11:54'), 4: Timestamp('2022-10-01 12:35:02'), 5: Timestamp('2022-10-04 08:34:23'), 6: Timestamp('2022-10-04 08:04:50'), 7: Timestamp('2022-10-04 17:17:47'), 8: Timestamp('2022-10-04 15:20:29'), 9: Timestamp('2022-10-05 07:54:14')}, 'TYPE_OPER_VALIDATION': {0: 3, 1: 1, 2: 1, 3: 3, 4: 3, 5: 3, 6: 1, 7: 1, 8: 1, 9: 1}, 'NUM_SERIE_SUPPORT': {0: '2036002771', 1: '2036002771', 2: '2036002771', 3: '2036002771', 4: '2036002771', 5: '2036002771', 6: '2036002771', 7: '2036002771', 8: '2036002771', 9: '2036002771'}}
df3 = pd.dataframe{'contact_id': {0: '9C9690B1-F8AC-EC11-9840', 1: '9C9690B1-F8AC-EC11-9840', 2: '9C9690B1-F8AC-EC11-9840', 3: '9C9690B1-F8AC-EC11-9840', 4: '9C9690B1-F8AC-EC11-9840', 5: '9C9690B1-F8AC-EC11-9840', 6: '9C9690B1-F8AC-EC11-9840', 7: '9C9690B1-F8AC-EC11-9840', 8: '9C9690B1-F8AC-EC11-9840', 9: '9C9690B1-F8AC-EC11-9840'}, 'DTHR_OPERATION_type3': {0: Timestamp('2022-10-01 08:30:18'), 1: Timestamp('2022-10-01 08:30:18'), 2: Timestamp('2022-10-01 08:30:18'), 3: Timestamp('2022-10-01 08:30:18'), 4: Timestamp('2022-10-01 08:30:18'), 5: Timestamp('2022-10-01 08:30:18'), 6: Timestamp('2022-10-01 08:30:18'), 7: Timestamp('2022-10-01 08:30:18'), 8: Timestamp('2022-10-01 08:30:18'), 9: Timestamp('2022-10-01 08:30:18')}, 'DTHR_OPERATION_type1': {0: Timestamp('2022-10-01 12:23:34'), 1: Timestamp('2022-10-01 07:47:46'), 2: Timestamp('2022-10-04 08:04:50'), 3: Timestamp('2022-10-04 17:17:47'), 4: Timestamp('2022-10-04 15:20:29'), 5: Timestamp('2022-10-05 07:54:14'), 6: Timestamp('2022-10-05 18:22:42'), 7: Timestamp('2022-10-06 08:14:28'), 8: Timestamp('2022-10-06 18:19:33'), 9: Timestamp('2022-10-08 07:46:45')}, 'seconds': {0: -13996.0, 1: 2552.0, 2: -257672.00000000003, 3: -290849.0, 4: -283811.0, 5: -343436.0, 6: -381144.0, 7: -431050.0, 8: -467355.00000000006, 9: -602187.0}, 'first_connection': {0: 'no', 1: 'yes', 2: 'no', 3: 'no', 4: 'no', 5: 'no', 6: 'no', 7: 'no', 8: 'no', 9: 'no'}}
df4 = pd.dataframe{'contact_id': {0: '9C9690B1-F8AC-EC11-9840', 1: '9C9690B1-F8AC-EC11-9840', 2: '9C9690B1-F8AC-EC11-9840', 3: '9C9690B1-F8AC-EC11-9840', 4: '9C9690B1-F8AC-EC11-9840', 5: '9C9690B1-F8AC-EC11-9840', 6: '9C9690B1-F8AC-EC11-9840', 7: '9C9690B1-F8AC-EC11-9840', 8: '9C9690B1-F8AC-EC11-9840', 9: '9C9690B1-F8AC-EC11-9840'}, 'DTHR_OPERATION_type3': {0: Timestamp('2022-10-01 08:30:18'), 1: Timestamp('2022-10-01 08:30:18'), 2: Timestamp('2022-10-01 08:30:18'), 3: Timestamp('2022-10-01 08:30:18'), 4: Timestamp('2022-10-01 08:30:18'), 5: Timestamp('2022-10-01 08:30:18'), 6: Timestamp('2022-10-01 08:30:18'), 7: Timestamp('2022-10-01 08:30:18'), 8: Timestamp('2022-10-01 08:30:18'), 9: Timestamp('2022-10-01 08:30:18')}, 'DTHR_OPERATION_type3bis': {0: Timestamp('2022-10-01 08:30:18'), 1: Timestamp('2022-10-01 13:11:54'), 2: Timestamp('2022-10-01 12:35:02'), 3: Timestamp('2022-10-04 08:34:23'), 4: Timestamp('2022-10-05 08:27:04'), 5: Timestamp('2022-10-05 19:05:29'), 6: Timestamp('2022-10-06 08:34:21'), 7: Timestamp('2022-10-06 18:37:56'), 8: Timestamp('2022-10-06 19:08:30'), 9: Timestamp('2022-10-08 13:01:13')}, 'seconds_type3': {0: 0.0, 1: -16896.0, 2: -14684.000000000002, 3: -259445.00000000003, 4: -345406.0, 5: -383711.0, 6: -432243.0, 7: -468458.00000000006, 8: -470292.00000000006, 9: -621055.0}, 'second_or_more_connection': {0: 'no', 1: 'no', 2: 'no', 3: 'no', 4: 'no', 5: 'no', 6: 'no', 7: 'no', 8: 'no', 9: 'no'}}
The desired result is a dF5 with the following columns [['contact_id', 'fullname', 'validation_id', 'LIBEL_LONG_PRODUIT_TITRE', 'TYPE_OPER_VALIDATION']] as well as this new colum dF5['connection]. Don't hestitate to reach out if you need further information or clarifications. Many thanks for your support :)

Give multiple dictionaries can I get the lowest(or highest)value for each key?

I'm not sure how to approach this problem but given the following dict:
{'diff': {0: 358438.3179047619, 1: 2877912.924419369, 2: 822017.9039274186, 3: 4914425.223282051, 4: 574184.9971827588, 5: 7432268.5341428565, 6: 1111639.5132252753, 7: 1322861.412610346, 8: 1179799.2592362808, 9: 87556.64146904761}}
{'diff': {0: 292811.4124761905, 1: 2831096.9336261265, 2: 760006.755798387, 3: 4868369.423293451, 4: 509515.30310344836, 5: 7390444.080714285, 6: 1028933.0801098899, 7: 1240273.4906724147, 8: 1138039.7093932922, 9: 43618.81660000001}}
{'diff': {0: 393148.40700238093, 1: 2923931.0134306327, 2: 878450.4552137096, 3: 4962539.102763245, 4: 660218.1550965513, 5: 7483527.590967346, 6: 1223029.819152747, 7: 1372622.6893804593, 8: 1202322.4719079277, 9: 113611.58858809523}}
{'diff': {0: 386402.65016666666, 1: 2916900.423062612, 2: 870947.0239475806, 3: 4954526.795990028, 4: 652106.3039551723, 5: 7475754.573836735, 6: 1212934.2664368134, 7: 1365836.4194977009, 8: 1196003.2297920743, 9: 108039.20073571429}}
{'diff': {0: 349975.29688095255, 1: 2876674.3017342356, 2: 827975.0650000006, 3: 4913329.426507118, 4: 605245.163706897, 5: 7431737.75197959, 6: 1154341.8745934067, 7: 1325611.1466724137, 8: 1167062.6884146344, 9: 78813.5207857143}}
{'diff': {0: 389236.3094642856, 1: 2919969.395930179, 2: 873295.801427419, 3: 4957163.9330507135, 4: 653377.0037568965, 5: 7479596.044428572, 6: 1214463.8978571433, 7: 1366351.4634890805, 8: 1200255.7743564018, 9: 112641.91081666667}}
{'diff': {0: 391681.69095, 1: 2921278.030853604, 2: 874417.996964516, 3: 4960328.984978635, 4: 658758.8998741381, 5: 7484168.382208164, 6: 1218278.5344219788, 7: 1367466.964590805, 8: 1200111.4596570123, 9: 113533.64980238095}}
{'diff': {0: 355994.5180714284, 1: 2882303.7541306294, 2: 835458.8338790324, 3: 4919442.302396014, 4: 610290.0786551724, 5: 7441912.343979592, 6: 1164700.055917583, 7: 1327737.2043103438, 8: 1169616.6454146332, 9: 81680.70286904761}}
{'diff': {0: 379893.7180714286, 1: 2913403.720793244, 2: 865857.7399225802, 3: 4948973.331188316, 4: 643761.719862069, 5: 7468621.204883674, 6: 1209897.9149901094, 7: 1359244.204440804, 8: 1192828.6090381108, 9: 104051.28336904761}}
{'diff': {0: 390466.6839142858, 1: 2923088.262698646, 2: 877156.1510145164, 3: 4962513.822048144, 4: 659759.9533551724, 5: 7483875.484744897, 6: 1222339.2461901105, 7: 1369121.0132643674, 8: 1201501.5448817061, 9: 113458.57306428571}}
{'diff': {0: 301792.62588095234, 1: 2854027.945333335, 2: 804759.8740564514, 3: 4876267.124210826, 4: 584088.0599310346, 5: 7378153.378530612, 6: 1133044.61306044, 7: 1291385.6421149436, 8: 1139054.1821890248, 9: 38275.36907142856}}
{'diff': {0: 387509.1658071429, 1: 2919049.8491373872, 2: 874219.6323653222, 3: 4955459.435102557, 4: 656559.3065396551, 5: 7476533.654826531, 6: 1217855.0112197807, 7: 1366842.1718931037, 8: 1198388.2114634141, 9: 108848.47544047613}}
{'diff': {0: 377328.5187738094, 1: 2907686.5556463962, 2: 861963.8367822578, 3: 4942903.962752138, 4: 642356.8619948275, 5: 7463152.797857141, 6: 1203804.631930769, 7: 1356454.3497155162, 8: 1189309.752909755, 9: 99476.27148809524}}
{'diff': {0: 352355.7500238095, 1: 2887318.768563064, 2: 841642.5822338712, 3: 4925029.717854701, 4: 621227.5312931032, 5: 7443790.6748775495, 6: 1183558.6595329673, 7: 1333697.2241666662, 8: 1172889.8671798778, 9: 81039.74188095241}}
{'diff': {0: 396255.3198571428, 1: 2926250.0441639633, 2: 880795.3943693547, 3: 4965277.919590886, 4: 663756.0494362068, 5: 7487330.063967346, 6: 1225360.306425824, 7: 1374148.3940419543, 8: 1204383.4553957311, 9: 117133.45492380955}}
{'diff': {0: 397275.22611428564, 1: 2928138.3937932434, 2: 882549.978358064, 3: 4967271.384024783, 4: 665063.7757241379, 5: 7489353.048779594, 6: 1227848.7195598893, 7: 1375612.8537936783, 8: 1205968.5550199081, 9: 118622.92846666674}}
{'diff': {0: 370638.9714999999, 1: 2901794.814063063, 2: 854231.343169355, 3: 4941840.968413107, 4: 636963.8949827587, 5: 7462906.844836734, 6: 1198474.5955769236, 7: 1349199.7593390818, 8: 1181772.3528810989, 9: 94418.88628571431}}
{'diff': {0: 399605.39451595227, 1: 2930519.3274677014, 2: 884866.901809758, 3: 4970067.843492109, 4: 668209.9673794828, 5: 7492181.322271633, 6: 1230438.6087753302, 7: 1377940.4613927014, 8: 1207999.2446168917, 9: 120766.5979569048}}
{'diff': {0: 394437.6273380953, 1: 2926444.621315316, 2: 880587.8419403222, 3: 4965842.826658971, 4: 663091.0379724137, 5: 7487427.719579593, 6: 1226653.0014609892, 7: 1373195.8957902302, 8: 1204177.9670981697, 9: 116665.88206190476}}
{'diff': {0: 343177.5738333332, 1: 2872438.88899099, 2: 824308.511145161, 3: 4901171.498498574, 4: 594996.441275862, 5: 7417912.784775511, 6: 1150261.9712527473, 7: 1323742.7629367814, 8: 1160229.847768293, 9: 70927.47897619048}}
{'diff': {0: 388380.7712333334, 1: 2919408.214353603, 2: 872090.4287451615, 3: 4957030.500496009, 4: 653652.7608896552, 5: 7478706.309169387, 6: 1210682.681124176, 7: 1365970.9027482753, 8: 1199850.2533893296, 9: 112281.24546190478}}
{'diff': {0: 397734.3032357143, 1: 2928578.1657990995, 2: 883142.2520741936, 3: 4967757.699845867, 4: 666066.6204482759, 5: 7489537.145657143, 6: 1228560.9616604394, 7: 1376307.6271563205, 8: 1206326.1817006094, 9: 118790.83264523809}}
{'diff': {0: 382516.58267857146, 1: 2915073.5680945935, 2: 869510.7518991937, 3: 4954054.122938737, 4: 651079.4144172415, 5: 7474328.030612243, 6: 1213478.4472478013, 7: 1363524.8940068972, 8: 1194637.4700762194, 9: 106180.7426238095}}
{'diff': {0: 395288.79071904765, 1: 2925967.5104626133, 2: 880203.2222838707, 3: 4964695.553390598, 4: 663391.0626017239, 5: 7486925.765212244, 6: 1224951.42912967, 7: 1373539.6626603457, 8: 1204264.0033243895, 9: 116594.66418571424}}
{'diff': {0: 397971.03177380946, 1: 2928499.596860811, 2: 882838.8586330643, 3: 4967899.803066952, 4: 665805.6550189656, 5: 7489992.297071429, 6: 1227996.4815417586, 7: 1376172.2323091957, 8: 1206288.5885036567, 9: 119011.36759404762}}
{'diff': {0: 381045.4717000001, 1: 2915758.7289301776, 2: 868614.6180701618, 3: 4952364.463031057, 4: 649488.6040396551, 5: 7473145.036408164, 6: 1211084.349763737, 7: 1359986.3620787358, 8: 1195206.9199817067, 9: 106315.4963142857}}
{'diff': {0: 396112.6919309524, 1: 2927023.3355063056, 2: 881553.0804177421, 3: 4965659.387115391, 4: 664581.2356241376, 5: 7487379.988112247, 6: 1226928.2231780214, 7: 1375081.4878034485, 8: 1204924.4063521333, 9: 117568.09009999997}}
{'diff': {0: 398791.83564142865, 1: 2929904.1134937378, 2: 884268.6928083871, 3: 4969186.882990996, 4: 667453.0766655172, 5: 7491095.65462857, 6: 1229856.0675498352, 7: 1377319.7854759197, 8: 1207308.6914243596, 9: 119888.01348333333}}
{'diff': {0: 361949.4825238095, 1: 2896682.3701126124, 2: 848862.5437822583, 3: 4928751.334897436, 4: 630220.5688913792, 5: 7450946.972428572, 6: 1187394.4575274729, 7: 1340303.0000775873, 8: 1179480.5218445128, 9: 87392.22891666667}}
{'diff': {0: 315083.9590238095, 1: 2875386.155256756, 2: 791020.9478145165, 3: 4919627.580308269, 4: 527800.8608620691, 5: 7418705.913040818, 6: 1038398.8350329669, 7: 1278070.7195321836, 8: 1177528.7164743906, 9: 74112.39018833333}}
{'diff': {0: 372749.6816428571, 1: 2896460.682382884, 2: 847202.8589435485, 3: 4930094.333652413, 4: 625970.5209655174, 5: 7459734.096877551, 6: 1180734.9652692305, 7: 1343552.4978419545, 8: 1181099.5920807915, 9: 96045.81380238094}}
{'diff': {0: 344613.2344047619, 1: 2879554.2198873875, 2: 832675.4379758065, 3: 4903486.329074071, 4: 607037.1537931036, 5: 7421040.479510205, 6: 1162536.2775054947, 7: 1323128.7494655175, 8: 1167686.5103018305, 9: 71892.5343452381}}
{'diff': {0: 384315.38414285716, 1: 2912584.135851351, 2: 868077.15266129, 3: 4948184.254348999, 4: 649068.8833655174, 5: 7468412.446755102, 6: 1210267.5453626376, 7: 1363363.9941091961, 8: 1193581.5346847544, 9: 105967.95393333334}}
{'diff': {0: 301436.47307142866, 1: 2814021.3400585586, 2: 745901.4201774193, 3: 4840460.746145298, 4: 474416.3661724136, 5: 7368423.201306123, 6: 956427.8998351648, 7: 1251712.683614943, 8: 1124866.8502560984, 9: 39000.96757142855}}
{'diff': {0: 350845.38038095244, 1: 2877748.116752253, 2: 830765.2659354841, 3: 4904564.910629633, 4: 609597.3515431035, 5: 7433322.860551022, 6: 1165465.3320219782, 7: 1328359.751505749, 8: 1164311.8769268298, 9: 75226.42738095239}}
{'diff': {0: 273791.3171666667, 1: 2806945.4198468463, 2: 722071.8942903227, 3: 4835423.34654701, 4: -74570571675091.14, 5: 7366856.7167755095, 6: 949178.8157032969, 7: 1234820.710781609, 8: 1113428.221018293, 9: 19590.516166666657}}
For each key 0-9, in my case, it is larger. I want an outcome where there is one dictionary with the lowest value for each key across all the different dictionaries.
so if, we have:
{'diff': {0: 5, 1: 4, 2: 3, 3: 43, 4: -34, 5: 43, 6: 65, 7: 543, 8: 23, 9: 23}}
{'diff': {0: 6, 1: 3, 2: 8, 3: 78, 4: -23, 5: 54, 6: 76, 7: 43, 8: 234, 9: 54}}
Then I would expect:
{'diff': {0: 5, 1: 3, 2: 3, 3: 43, 4: -23, 5: 43, 6: 65, 7: 43, 8: 23, 9: 23}}
update: when you print the list of dicts, you get:
[{'diff': {0: 358438.3179047619, 1: 2877912.924419369, 2: 822017.9039274186, 3: 4914425.223282051, 4: 574184.9971827588, 5: 7432268.5341428565, 6: 1111639.5132252753, 7: 1322861.412610346, 8: 1179799.2592362808, 9: 87556.64146904761}}, {'diff': {0: 292811.4124761905, 1: 2831096.9336261265, 2: 760006.755798387, 3: 4868369.423293451, 4: 509515.30310344836, 5: 7390444.080714285, 6: 1028933.0801098899, 7: 1240273.4906724147, 8: 1138039.7093932922, 9: 43618.81660000001}}, {'diff': {0: 393148.40700238093, 1: 2923931.0134306327, 2: 878450.4552137096, 3: 4962539.102763245, 4: 660218.1550965513, 5: 7483527.590967346, 6: 1223029.819152747, 7: 1372622.6893804593, 8: 1202322.4719079277, 9: 113611.58858809523}}, {'diff': {0: 386402.65016666666, 1: 2916900.423062612, 2: 870947.0239475806, 3: 4954526.795990028, 4: 652106.3039551723, 5: 7475754.573836735, 6: 1212934.2664368134, 7: 1365836.4194977009, 8: 1196003.2297920743, 9: 108039.20073571429}}, {'diff': {0: 349975.29688095255, 1: 2876674.3017342356, 2: 827975.0650000006, 3: 4913329.426507118, 4: 605245.163706897, 5: 7431737.75197959, 6: 1154341.8745934067, 7: 1325611.1466724137, 8: 1167062.6884146344, 9: 78813.5207857143}}, {'diff': {0: 389236.3094642856, 1: 2919969.395930179, 2: 873295.801427419, 3: 4957163.9330507135, 4: 653377.0037568965, 5: 7479596.044428572, 6: 1214463.8978571433, 7: 1366351.4634890805, 8: 1200255.7743564018, 9: 112641.91081666667}}, {'diff': {0: 391681.69095, 1: 2921278.030853604, 2: 874417.996964516, 3: 4960328.984978635, 4: 658758.8998741381, 5: 7484168.382208164, 6: 1218278.5344219788, 7: 1367466.964590805, 8: 1200111.4596570123, 9: 113533.64980238095}}, {'diff': {0: 355994.5180714284, 1: 2882303.7541306294, 2: 835458.8338790324, 3: 4919442.302396014, 4: 610290.0786551724, 5: 7441912.343979592, 6: 1164700.055917583, 7: 1327737.2043103438, 8: 1169616.6454146332, 9: 81680.70286904761}}, {'diff': {0: 379893.7180714286, 1: 2913403.720793244, 2: 865857.7399225802, 3: 4948973.331188316, 4: 643761.719862069, 5: 7468621.204883674, 6: 1209897.9149901094, 7: 1359244.204440804, 8: 1192828.6090381108, 9: 104051.28336904761}}, {'diff': {0: 390466.6839142858, 1: 2923088.262698646, 2: 877156.1510145164, 3: 4962513.822048144, 4: 659759.9533551724, 5: 7483875.484744897, 6: 1222339.2461901105, 7: 1369121.0132643674, 8: 1201501.5448817061, 9: 113458.57306428571}}, {'diff': {0: 301792.62588095234, 1: 2854027.945333335, 2: 804759.8740564514, 3: 4876267.124210826, 4: 584088.0599310346, 5: 7378153.378530612, 6: 1133044.61306044, 7: 1291385.6421149436, 8: 1139054.1821890248, 9: 38275.36907142856}}, {'diff': {0: 387509.1658071429, 1: 2919049.8491373872, 2: 874219.6323653222, 3: 4955459.435102557, 4: 656559.3065396551, 5: 7476533.654826531, 6: 1217855.0112197807, 7: 1366842.1718931037, 8: 1198388.2114634141, 9: 108848.47544047613}}, {'diff': {0: 377328.5187738094, 1: 2907686.5556463962, 2: 861963.8367822578, 3: 4942903.962752138, 4: 642356.8619948275, 5: 7463152.797857141, 6: 1203804.631930769, 7: 1356454.3497155162, 8: 1189309.752909755, 9: 99476.27148809524}}, {'diff': {0: 352355.7500238095, 1: 2887318.768563064, 2: 841642.5822338712, 3: 4925029.717854701, 4: 621227.5312931032, 5: 7443790.6748775495, 6: 1183558.6595329673, 7: 1333697.2241666662, 8: 1172889.8671798778, 9: 81039.74188095241}}, {'diff': {0: 396255.3198571428, 1: 2926250.0441639633, 2: 880795.3943693547, 3: 4965277.919590886, 4: 663756.0494362068, 5: 7487330.063967346, 6: 1225360.306425824, 7: 1374148.3940419543, 8: 1204383.4553957311, 9: 117133.45492380955}}, {'diff': {0: 397275.22611428564, 1: 2928138.3937932434, 2: 882549.978358064, 3: 4967271.384024783, 4: 665063.7757241379, 5: 7489353.048779594, 6: 1227848.7195598893, 7: 1375612.8537936783, 8: 1205968.5550199081, 9: 118622.92846666674}}, {'diff': {0: 370638.9714999999, 1: 2901794.814063063, 2: 854231.343169355, 3: 4941840.968413107, 4: 636963.8949827587, 5: 7462906.844836734, 6: 1198474.5955769236, 7: 1349199.7593390818, 8: 1181772.3528810989, 9: 94418.88628571431}}, {'diff': {0: 399605.39451595227, 1: 2930519.3274677014, 2: 884866.901809758, 3: 4970067.843492109, 4: 668209.9673794828, 5: 7492181.322271633, 6: 1230438.6087753302, 7: 1377940.4613927014, 8: 1207999.2446168917, 9: 120766.5979569048}}, {'diff': {0: 394437.6273380953, 1: 2926444.621315316, 2: 880587.8419403222, 3: 4965842.826658971, 4: 663091.0379724137, 5: 7487427.719579593, 6: 1226653.0014609892, 7: 1373195.8957902302, 8: 1204177.9670981697, 9: 116665.88206190476}}, {'diff': {0: 343177.5738333332, 1: 2872438.88899099, 2: 824308.511145161, 3: 4901171.498498574, 4: 594996.441275862, 5: 7417912.784775511, 6: 1150261.9712527473, 7: 1323742.7629367814, 8: 1160229.847768293, 9: 70927.47897619048}}, {'diff': {0: 388380.7712333334, 1: 2919408.214353603, 2: 872090.4287451615, 3: 4957030.500496009, 4: 653652.7608896552, 5: 7478706.309169387, 6: 1210682.681124176, 7: 1365970.9027482753, 8: 1199850.2533893296, 9: 112281.24546190478}}, {'diff': {0: 397734.3032357143, 1: 2928578.1657990995, 2: 883142.2520741936, 3: 4967757.699845867, 4: 666066.6204482759, 5: 7489537.145657143, 6: 1228560.9616604394, 7: 1376307.6271563205, 8: 1206326.1817006094, 9: 118790.83264523809}}, {'diff': {0: 382516.58267857146, 1: 2915073.5680945935, 2: 869510.7518991937, 3: 4954054.122938737, 4: 651079.4144172415, 5: 7474328.030612243, 6: 1213478.4472478013, 7: 1363524.8940068972, 8: 1194637.4700762194, 9: 106180.7426238095}}, {'diff': {0: 395288.79071904765, 1: 2925967.5104626133, 2: 880203.2222838707, 3: 4964695.553390598, 4: 663391.0626017239, 5: 7486925.765212244, 6: 1224951.42912967, 7: 1373539.6626603457, 8: 1204264.0033243895, 9: 116594.66418571424}}, {'diff': {0: 397971.03177380946, 1: 2928499.596860811, 2: 882838.8586330643, 3: 4967899.803066952, 4: 665805.6550189656, 5: 7489992.297071429, 6: 1227996.4815417586, 7: 1376172.2323091957, 8: 1206288.5885036567, 9: 119011.36759404762}}, {'diff': {0: 381045.4717000001, 1: 2915758.7289301776, 2: 868614.6180701618, 3: 4952364.463031057, 4: 649488.6040396551, 5: 7473145.036408164, 6: 1211084.349763737, 7: 1359986.3620787358, 8: 1195206.9199817067, 9: 106315.4963142857}}, {'diff': {0: 396112.6919309524, 1: 2927023.3355063056, 2: 881553.0804177421, 3: 4965659.387115391, 4: 664581.2356241376, 5: 7487379.988112247, 6: 1226928.2231780214, 7: 1375081.4878034485, 8: 1204924.4063521333, 9: 117568.09009999997}}, {'diff': {0: 398791.83564142865, 1: 2929904.1134937378, 2: 884268.6928083871, 3: 4969186.882990996, 4: 667453.0766655172, 5: 7491095.65462857, 6: 1229856.0675498352, 7: 1377319.7854759197, 8: 1207308.6914243596, 9: 119888.01348333333}}, {'diff': {0: 361949.4825238095, 1: 2896682.3701126124, 2: 848862.5437822583, 3: 4928751.334897436, 4: 630220.5688913792, 5: 7450946.972428572, 6: 1187394.4575274729, 7: 1340303.0000775873, 8: 1179480.5218445128, 9: 87392.22891666667}}, {'diff': {0: 315083.9590238095, 1: 2875386.155256756, 2: 791020.9478145165, 3: 4919627.580308269, 4: 527800.8608620691, 5: 7418705.913040818, 6: 1038398.8350329669, 7: 1278070.7195321836, 8: 1177528.7164743906, 9: 74112.39018833333}}, {'diff': {0: 372749.6816428571, 1: 2896460.682382884, 2: 847202.8589435485, 3: 4930094.333652413, 4: 625970.5209655174, 5: 7459734.096877551, 6: 1180734.9652692305, 7: 1343552.4978419545, 8: 1181099.5920807915, 9: 96045.81380238094}}, {'diff': {0: 344613.2344047619, 1: 2879554.2198873875, 2: 832675.4379758065, 3: 4903486.329074071, 4: 607037.1537931036, 5: 7421040.479510205, 6: 1162536.2775054947, 7: 1323128.7494655175, 8: 1167686.5103018305, 9: 71892.5343452381}}, {'diff': {0: 384315.38414285716, 1: 2912584.135851351, 2: 868077.15266129, 3: 4948184.254348999, 4: 649068.8833655174, 5: 7468412.446755102, 6: 1210267.5453626376, 7: 1363363.9941091961, 8: 1193581.5346847544, 9: 105967.95393333334}}, {'diff': {0: 301436.47307142866, 1: 2814021.3400585586, 2: 745901.4201774193, 3: 4840460.746145298, 4: 474416.3661724136, 5: 7368423.201306123, 6: 956427.8998351648, 7: 1251712.683614943, 8: 1124866.8502560984, 9: 39000.96757142855}}, {'diff': {0: 350845.38038095244, 1: 2877748.116752253, 2: 830765.2659354841, 3: 4904564.910629633, 4: 609597.3515431035, 5: 7433322.860551022, 6: 1165465.3320219782, 7: 1328359.751505749, 8: 1164311.8769268298, 9: 75226.42738095239}}, {'diff': {0: 273791.3171666667, 1: 2806945.4198468463, 2: 722071.8942903227, 3: 4835423.34654701, 4: -74570571675091.14, 5: 7366856.7167755095, 6: 949178.8157032969, 7: 1234820.710781609, 8: 1113428.221018293, 9: 19590.516166666657}}]

This is a classical reduce problem, so one approach is to use the built-in function functools.reduce:
from functools import reduce
def min_(x, y, key="diff"):
return { key : { ki : min(xi, y[key][ki]) for ki, xi in x[key].items() } }
res = reduce(min_, data)
print(res)
Output (for the given data)
{'diff': {0: 273791.3171666667, 1: 2806945.4198468463, 2: 722071.8942903227, 3: 4835423.34654701, 4: -74570571675091.14, 5: 7366856.7167755095, 6: 949178.8157032969, 7: 1234820.710781609, 8: 1113428.221018293, 9: 19590.516166666657}}
As an alternative, you could use pandas, as below:
import pandas as pd
# assuming data is a list of dictionaries with the same format of the question
res = {"diff": pd.DataFrame(data=[d["diff"] for d in data]).min().to_dict()}
print(res)
Output (using pandas)
{'diff': {0: 273791.3171666667, 1: 2806945.4198468463, 2: 722071.8942903227, 3: 4835423.34654701, 4: -74570571675091.14, 5: 7366856.7167755095, 6: 949178.8157032969, 7: 1234820.710781609, 8: 1113428.221018293, 9: 19590.516166666657}}
Note that pandas is a (heavy) third-party library that needs to be installed.

You can do it with a dictionary comprehension that calls min() across all the dictionaries in the list.
result = {'diff':
{key: min(item['diff'][key] for item in list_of_dicts)
for key in list_of_dicts[0]['diff']}
}

A combination of zip and map can do this for you:
Input:
dicts = [ {'diff': {0: 358438.3179047619, 1: 2877912.924419369, 2: 822017.9039274186, 3: 4914425.223282051, 4: 574184.9971827588, 5: 7432268.5341428565, 6: 1111639.5132252753, 7: 1322861.412610346, 8: 1179799.2592362808, 9: 87556.64146904761}},
{'diff': {0: 292811.4124761905, 1: 2831096.9336261265, 2: 760006.755798387, 3: 4868369.423293451, 4: 509515.30310344836, 5: 7390444.080714285, 6: 1028933.0801098899, 7: 1240273.4906724147, 8: 1138039.7093932922, 9: 43618.81660000001}},
{'diff': {0: 393148.40700238093, 1: 2923931.0134306327, 2: 878450.4552137096, 3: 4962539.102763245, 4: 660218.1550965513, 5: 7483527.590967346, 6: 1223029.819152747, 7: 1372622.6893804593, 8: 1202322.4719079277, 9: 113611.58858809523}},
{'diff': {0: 386402.65016666666, 1: 2916900.423062612, 2: 870947.0239475806, 3: 4954526.795990028, 4: 652106.3039551723, 5: 7475754.573836735, 6: 1212934.2664368134, 7: 1365836.4194977009, 8: 1196003.2297920743, 9: 108039.20073571429}},
{'diff': {0: 349975.29688095255, 1: 2876674.3017342356, 2: 827975.0650000006, 3: 4913329.426507118, 4: 605245.163706897, 5: 7431737.75197959, 6: 1154341.8745934067, 7: 1325611.1466724137, 8: 1167062.6884146344, 9: 78813.5207857143}},
{'diff': {0: 389236.3094642856, 1: 2919969.395930179, 2: 873295.801427419, 3: 4957163.9330507135, 4: 653377.0037568965, 5: 7479596.044428572, 6: 1214463.8978571433, 7: 1366351.4634890805, 8: 1200255.7743564018, 9: 112641.91081666667}},
{'diff': {0: 391681.69095, 1: 2921278.030853604, 2: 874417.996964516, 3: 4960328.984978635, 4: 658758.8998741381, 5: 7484168.382208164, 6: 1218278.5344219788, 7: 1367466.964590805, 8: 1200111.4596570123, 9: 113533.64980238095}},
{'diff': {0: 355994.5180714284, 1: 2882303.7541306294, 2: 835458.8338790324, 3: 4919442.302396014, 4: 610290.0786551724, 5: 7441912.343979592, 6: 1164700.055917583, 7: 1327737.2043103438, 8: 1169616.6454146332, 9: 81680.70286904761}},
{'diff': {0: 379893.7180714286, 1: 2913403.720793244, 2: 865857.7399225802, 3: 4948973.331188316, 4: 643761.719862069, 5: 7468621.204883674, 6: 1209897.9149901094, 7: 1359244.204440804, 8: 1192828.6090381108, 9: 104051.28336904761}},
{'diff': {0: 390466.6839142858, 1: 2923088.262698646, 2: 877156.1510145164, 3: 4962513.822048144, 4: 659759.9533551724, 5: 7483875.484744897, 6: 1222339.2461901105, 7: 1369121.0132643674, 8: 1201501.5448817061, 9: 113458.57306428571}},
{'diff': {0: 301792.62588095234, 1: 2854027.945333335, 2: 804759.8740564514, 3: 4876267.124210826, 4: 584088.0599310346, 5: 7378153.378530612, 6: 1133044.61306044, 7: 1291385.6421149436, 8: 1139054.1821890248, 9: 38275.36907142856}},
{'diff': {0: 387509.1658071429, 1: 2919049.8491373872, 2: 874219.6323653222, 3: 4955459.435102557, 4: 656559.3065396551, 5: 7476533.654826531, 6: 1217855.0112197807, 7: 1366842.1718931037, 8: 1198388.2114634141, 9: 108848.47544047613}},
{'diff': {0: 377328.5187738094, 1: 2907686.5556463962, 2: 861963.8367822578, 3: 4942903.962752138, 4: 642356.8619948275, 5: 7463152.797857141, 6: 1203804.631930769, 7: 1356454.3497155162, 8: 1189309.752909755, 9: 99476.27148809524}},
{'diff': {0: 352355.7500238095, 1: 2887318.768563064, 2: 841642.5822338712, 3: 4925029.717854701, 4: 621227.5312931032, 5: 7443790.6748775495, 6: 1183558.6595329673, 7: 1333697.2241666662, 8: 1172889.8671798778, 9: 81039.74188095241}},
{'diff': {0: 396255.3198571428, 1: 2926250.0441639633, 2: 880795.3943693547, 3: 4965277.919590886, 4: 663756.0494362068, 5: 7487330.063967346, 6: 1225360.306425824, 7: 1374148.3940419543, 8: 1204383.4553957311, 9: 117133.45492380955}},
{'diff': {0: 397275.22611428564, 1: 2928138.3937932434, 2: 882549.978358064, 3: 4967271.384024783, 4: 665063.7757241379, 5: 7489353.048779594, 6: 1227848.7195598893, 7: 1375612.8537936783, 8: 1205968.5550199081, 9: 118622.92846666674}},
{'diff': {0: 370638.9714999999, 1: 2901794.814063063, 2: 854231.343169355, 3: 4941840.968413107, 4: 636963.8949827587, 5: 7462906.844836734, 6: 1198474.5955769236, 7: 1349199.7593390818, 8: 1181772.3528810989, 9: 94418.88628571431}},
{'diff': {0: 399605.39451595227, 1: 2930519.3274677014, 2: 884866.901809758, 3: 4970067.843492109, 4: 668209.9673794828, 5: 7492181.322271633, 6: 1230438.6087753302, 7: 1377940.4613927014, 8: 1207999.2446168917, 9: 120766.5979569048}},
{'diff': {0: 394437.6273380953, 1: 2926444.621315316, 2: 880587.8419403222, 3: 4965842.826658971, 4: 663091.0379724137, 5: 7487427.719579593, 6: 1226653.0014609892, 7: 1373195.8957902302, 8: 1204177.9670981697, 9: 116665.88206190476}},
{'diff': {0: 343177.5738333332, 1: 2872438.88899099, 2: 824308.511145161, 3: 4901171.498498574, 4: 594996.441275862, 5: 7417912.784775511, 6: 1150261.9712527473, 7: 1323742.7629367814, 8: 1160229.847768293, 9: 70927.47897619048}},
{'diff': {0: 388380.7712333334, 1: 2919408.214353603, 2: 872090.4287451615, 3: 4957030.500496009, 4: 653652.7608896552, 5: 7478706.309169387, 6: 1210682.681124176, 7: 1365970.9027482753, 8: 1199850.2533893296, 9: 112281.24546190478}},
{'diff': {0: 397734.3032357143, 1: 2928578.1657990995, 2: 883142.2520741936, 3: 4967757.699845867, 4: 666066.6204482759, 5: 7489537.145657143, 6: 1228560.9616604394, 7: 1376307.6271563205, 8: 1206326.1817006094, 9: 118790.83264523809}},
{'diff': {0: 382516.58267857146, 1: 2915073.5680945935, 2: 869510.7518991937, 3: 4954054.122938737, 4: 651079.4144172415, 5: 7474328.030612243, 6: 1213478.4472478013, 7: 1363524.8940068972, 8: 1194637.4700762194, 9: 106180.7426238095}},
{'diff': {0: 395288.79071904765, 1: 2925967.5104626133, 2: 880203.2222838707, 3: 4964695.553390598, 4: 663391.0626017239, 5: 7486925.765212244, 6: 1224951.42912967, 7: 1373539.6626603457, 8: 1204264.0033243895, 9: 116594.66418571424}},
{'diff': {0: 397971.03177380946, 1: 2928499.596860811, 2: 882838.8586330643, 3: 4967899.803066952, 4: 665805.6550189656, 5: 7489992.297071429, 6: 1227996.4815417586, 7: 1376172.2323091957, 8: 1206288.5885036567, 9: 119011.36759404762}},
{'diff': {0: 381045.4717000001, 1: 2915758.7289301776, 2: 868614.6180701618, 3: 4952364.463031057, 4: 649488.6040396551, 5: 7473145.036408164, 6: 1211084.349763737, 7: 1359986.3620787358, 8: 1195206.9199817067, 9: 106315.4963142857}},
{'diff': {0: 396112.6919309524, 1: 2927023.3355063056, 2: 881553.0804177421, 3: 4965659.387115391, 4: 664581.2356241376, 5: 7487379.988112247, 6: 1226928.2231780214, 7: 1375081.4878034485, 8: 1204924.4063521333, 9: 117568.09009999997}},
{'diff': {0: 398791.83564142865, 1: 2929904.1134937378, 2: 884268.6928083871, 3: 4969186.882990996, 4: 667453.0766655172, 5: 7491095.65462857, 6: 1229856.0675498352, 7: 1377319.7854759197, 8: 1207308.6914243596, 9: 119888.01348333333}},
{'diff': {0: 361949.4825238095, 1: 2896682.3701126124, 2: 848862.5437822583, 3: 4928751.334897436, 4: 630220.5688913792, 5: 7450946.972428572, 6: 1187394.4575274729, 7: 1340303.0000775873, 8: 1179480.5218445128, 9: 87392.22891666667}},
{'diff': {0: 315083.9590238095, 1: 2875386.155256756, 2: 791020.9478145165, 3: 4919627.580308269, 4: 527800.8608620691, 5: 7418705.913040818, 6: 1038398.8350329669, 7: 1278070.7195321836, 8: 1177528.7164743906, 9: 74112.39018833333}},
{'diff': {0: 372749.6816428571, 1: 2896460.682382884, 2: 847202.8589435485, 3: 4930094.333652413, 4: 625970.5209655174, 5: 7459734.096877551, 6: 1180734.9652692305, 7: 1343552.4978419545, 8: 1181099.5920807915, 9: 96045.81380238094}},
{'diff': {0: 344613.2344047619, 1: 2879554.2198873875, 2: 832675.4379758065, 3: 4903486.329074071, 4: 607037.1537931036, 5: 7421040.479510205, 6: 1162536.2775054947, 7: 1323128.7494655175, 8: 1167686.5103018305, 9: 71892.5343452381}},
{'diff': {0: 384315.38414285716, 1: 2912584.135851351, 2: 868077.15266129, 3: 4948184.254348999, 4: 649068.8833655174, 5: 7468412.446755102, 6: 1210267.5453626376, 7: 1363363.9941091961, 8: 1193581.5346847544, 9: 105967.95393333334}},
{'diff': {0: 301436.47307142866, 1: 2814021.3400585586, 2: 745901.4201774193, 3: 4840460.746145298, 4: 474416.3661724136, 5: 7368423.201306123, 6: 956427.8998351648, 7: 1251712.683614943, 8: 1124866.8502560984, 9: 39000.96757142855}},
{'diff': {0: 350845.38038095244, 1: 2877748.116752253, 2: 830765.2659354841, 3: 4904564.910629633, 4: 609597.3515431035, 5: 7433322.860551022, 6: 1165465.3320219782, 7: 1328359.751505749, 8: 1164311.8769268298, 9: 75226.42738095239}},
{'diff': {0: 273791.3171666667, 1: 2806945.4198468463, 2: 722071.8942903227, 3: 4835423.34654701, 4: -74570571675091.14, 5: 7366856.7167755095, 6: 949178.8157032969, 7: 1234820.710781609, 8: 1113428.221018293, 9: 19590.516166666657}}]
Output:
result = {'diff':dict(map(max,zip(*(d['diff'].items() for d in dicts))))}
print(result)
{'diff': {0: 399605.39451595227, 1: 2930519.3274677014,
2: 884866.901809758, 3: 4970067.843492109,
4: 668209.9673794828, 5: 7492181.322271633,
6: 1230438.6087753302, 7: 1377940.4613927014,
8: 1207999.2446168917, 9: 120766.5979569048}}
Note that this assumes that all 10 keys are always present and in the same order in every dictionary
If the keys are not always present or not in the same order, you could do this:
result = {'diff':{k:max(d['diff'].get(k,0) for d in dicts) for k in range(10)}}

How to export to excel with pandas dataframe with multi column

I'm stuck at exporting a multi index dataframe to excel, in the matter what I'm looking for.
This is what I'm looking for in excel.
I know I have to add an extra Index Parameter on the left for the row of SRR (%) and Traction (-), but how?
My code so far.
import pandas as pd
import matplotlib.pyplot as plt
data = {'Step 1': {'Step Typ': 'Traction', 'SRR (%)': {1: 8.384, 2: 9.815, 3: 7.531, 4: 10.209, 5: 7.989, 6: 7.331, 7: 5.008, 8: 2.716, 9: 9.6, 10: 7.911}, 'Traction (-)': {1: 5.602, 2: 6.04, 3: 2.631, 4: 2.952, 5: 8.162, 6: 9.312, 7: 4.994, 8: 2.959, 9: 10.075, 10: 5.498}, 'Temperature': 30, 'Load': 40}, 'Step 3': {'Step Typ': 'Traction', 'SRR (%)': {1: 2.909, 2: 5.552, 3: 5.656, 4: 9.043, 5: 3.424, 6: 7.382, 7: 3.916, 8: 2.665, 9: 4.832, 10: 3.993}, 'Traction (-)': {1: 9.158, 2: 6.721, 3: 7.787, 4: 7.491, 5: 8.267, 6: 2.985, 7: 5.882, 8: 3.591, 9: 6.334, 10: 10.43}, 'Temperature': 80, 'Load': 40}, 'Step 5': {'Step Typ': 'Traction', 'SRR (%)': {1: 4.765, 2: 9.293, 3: 7.608, 4: 7.371, 5: 4.87, 6: 4.832, 7: 6.244, 8: 6.488, 9: 5.04, 10: 2.962}, 'Traction (-)': {1: 6.656, 2: 7.872, 3: 8.799, 4: 7.9, 5: 4.22, 6: 6.288, 7: 7.439, 8: 7.77, 9: 5.977, 10: 9.395}, 'Temperature': 30, 'Load': 70}, 'Step 7': {'Step Typ': 'Traction', 'SRR (%)': {1: 9.46, 2: 2.83, 3: 3.249, 4: 9.273, 5: 8.792, 6: 9.673, 7: 6.784, 8: 3.838, 9: 8.779, 10: 4.82}, 'Traction (-)': {1: 5.245, 2: 8.491, 3: 10.088, 4: 9.988, 5: 4.886, 6: 4.168, 7: 8.628, 8: 5.038, 9: 7.712, 10: 3.961}, 'Temperature': 80, 'Load': 70} }
df = pd.DataFrame(data)
items = list()
series = list()
for item, d in data.items():
items.append(item)
series.append(pd.DataFrame.from_dict(d))
df = pd.concat(series, keys=items)
df.set_index(['Step Typ', 'Load', 'Temperature'], inplace=True).T.to_excel('testfile.xlsx')
The picture below, shows df.set_index(['Step Typ', 'Load', 'Temperature'], inplace=True).T as a dataframe: (somehow close, but not exactly what I'm looking for):
Edit 1:
Found a good solution, not the exact one I was looking for, but it's still worth using it.
df.reset_index().drop(["level_0","level_1"], axis=1).pivot(columns=["Step Typ", "Load", "Temperature"], values=["SRR (%)", "Traction (-)"]).apply(lambda x: pd.Series(x.dropna().values)).to_excel("solution.xlsx")

Can you explain clearely and show the output you are looking for?
To export a table to excel use df.to_excel('path', index=True/False)
where:
index=True or False - to insert or not the index column into the file

Found a good solution, not the exact one I was looking for, but it's still worth using it.
df.reset_index().drop(["level_0","level_1"], axis=1).pivot(columns=["Step Typ", "Load", "Temperature"], values=["SRR (%)", "Traction (-)"]).apply(lambda x: pd.Series(x.dropna().values)).to_excel("solution.xlsx")

How to smooth a pandas / matplotlib lineplot?

I have the following df, weekly spend in a number of shops:
shop1 shop2 shop3 shop4 shop5 shop6 shop7 \
date_week
2 4328.85 5058.17 3028.68 2513.28 4204.10 1898.26 2209.75
3 5472.00 5085.59 3874.51 1951.60 2984.71 1416.40 1199.42
4 4665.53 4264.05 2781.70 2958.25 4593.46 2365.88 2079.73
5 5769.36 3460.79 3072.47 1866.19 3803.12 2166.84 1716.71
6 6267.00 4033.58 4053.70 2215.04 3991.31 2382.02 1974.92
7 5436.83 4402.83 3225.98 1761.87 4202.22 2430.71 3091.33
8 4850.43 4900.68 3176.00 3280.95 3483.53 4115.09 2594.01
9 6782.88 3800.03 3865.65 2221.43 4116.28 2638.28 2321.55
10 6248.18 4096.60 5186.52 3224.96 3614.24 2541.00 2708.36
11 4505.18 2889.33 2937.74 2418.34 5565.57 1570.55 1371.54
12 3115.26 1216.82 1759.49 2559.81 1403.61 1550.77 478.34
13 4561.82 827.16 4661.51 3197.90 1515.63 1688.57 247.25
shop8 shop9
date_week
2 3578.81 3134.39
3 4625.10 2676.20
4 3417.16 3870.00
5 3980.78 3439.60
6 3899.42 4192.41
7 4190.60 3989.00
8 4786.40 3484.51
9 6433.02 3474.66
10 4414.19 3809.20
11 3590.10 3414.50
12 4297.57 2094.00
13 3963.27 871.25
If I plot these in a line plot or "spaghetti plot" It works fine.
The goal is the look at trend in weekly sales over the last three months in 9 stores.
But looks a bit messy:
newgraph.plot()
I had a look at similar questions such as this one which uses df.interpolate() but it looks like I need to have missing values in there first. this answer seems to require a time series.
Is there another method to smoothen out the lines?
It doesn't matter if the values are not exactly accurate anymore, some interpolation is fine. All I am interested in is the trend over the last number of weeks. I have also tried logy=True in the plot() method to calm the lines a bit, but it didn't help.
My df, for pd.DataFrame.fromt_dict():
{'shop1': {2: 4328.849999999999,
3: 5472.0,
4: 4665.530000000001,
5: 5769.36,
6: 6267.0,
7: 5436.83,
8: 4850.43,
9: 6782.879999999999,
10: 6248.18,
11: 4505.18,
12: 3115.26,
13: 4561.82},
'shop2': {2: 5058.169999999993,
3: 5085.589999999996,
4: 4264.049999999997,
5: 3460.7899999999977,
6: 4033.579999999998,
7: 4402.829999999999,
8: 4900.679999999997,
9: 3800.0299999999997,
10: 4096.5999999999985,
11: 2889.3300000000004,
12: 1216.8200000000002,
13: 827.16},
'shop3': {2: 3028.679999999997,
3: 3874.5099999999984,
4: 2781.6999999999994,
5: 3072.4699999999984,
6: 4053.6999999999966,
7: 3225.9799999999987,
8: 3175.9999999999973,
9: 3865.6499999999974,
10: 5186.519999999996,
11: 2937.74,
12: 1759.49,
13: 4661.509999999998},
'shop4': {2: 2513.2799999999997,
3: 1951.6000000000001,
4: 2958.25,
5: 1866.1900000000003,
6: 2215.04,
7: 1761.8700000000001,
8: 3280.9499999999994,
9: 2221.43,
10: 3224.9600000000005,
11: 2418.3399999999997,
12: 2559.8099999999995,
13: 3197.9},
'shop5': {2: 4204.0999999999985,
3: 2984.71,
4: 4593.459999999999,
5: 3803.12,
6: 3991.31,
7: 4202.219999999999,
8: 3483.529999999999,
9: 4116.279999999999,
10: 3614.24,
11: 5565.569999999997,
12: 1403.6100000000001,
13: 1515.63},
'shop6': {2: 1898.260000000001,
3: 1416.4000000000005,
4: 2365.8799999999997,
5: 2166.84,
6: 2382.019999999999,
7: 2430.71,
8: 4115.0899999999965,
9: 2638.2800000000007,
10: 2541.0,
11: 1570.5500000000004,
12: 1550.7700000000002,
13: 1688.5700000000004},
'shop7': {2: 2209.75,
3: 1199.42,
4: 2079.7300000000005,
5: 1716.7100000000005,
6: 1974.9200000000005,
7: 3091.329999999999,
8: 2594.0099999999993,
9: 2321.5499999999997,
10: 2708.3599999999983,
11: 1371.5400000000004,
12: 478.34,
13: 247.25000000000003},
'shop8': {2: 3578.8100000000004,
3: 4625.1,
4: 3417.1599999999994,
5: 3980.7799999999997,
6: 3899.4200000000005,
7: 4190.600000000001,
8: 4786.4,
9: 6433.019999999998,
10: 4414.1900000000005,
11: 3590.1,
12: 4297.57,
13: 3963.27},
'shop9': {2: 3134.3900000000003,
3: 2676.2,
4: 3870.0,
5: 3439.6,
6: 4192.41,
7: 3989.0,
8: 3484.51,
9: 3474.66,
10: 3809.2,
11: 3414.5,
12: 2094.0,
13: 871.25}}

You could show the trend by plotting a regression line for the last few weeks, perhaps separately from the actual data, as the plot is already so crowded. I would use seaborn, because it has the convenient regplot() function:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
df.plot(figsize=[12, 10], style='--')
plt.xlim(2, 18)
last4 = df[len(df)-4:]
plt.gca().set_prop_cycle(None)
for shop in df.columns:
sns.regplot(last4.index + 4, shop, data=last4, ci=None, scatter=False)
plt.ylabel(None)
plt.xticks(list(df.index)+[14, 17], labels=list(df.index)+[10, 13]);

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