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How to sort date index of a pandas dataframe so that all the newer year dates are on one side on the X-axis label when plotted on graph
Pandas timeseries plot setting x-axis major and minor ticks and labelsHow can I replace all the NaN values with Zero's in a column of a pandas dataframeHow to sort a dataFrame in python pandas by two or more columns?Constructing pandas DataFrame from values in variables gives “ValueError: If using all scalar values, you must pass an index”How to convert index of a pandas dataframe into a column?How to sort a Pandas DataFrame by index?Sort Pandas Dataframe by Datehow to sort pandas dataframe from one columnPlot pandas dataframe index formatted as Month-Year on x axisHow to plot pandas DataFrame with date (Year/Month)?
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I have a pandas dataframe with dates as the indexes (indices). When I plot the values, the indexes(dates, i.e. the X-axis labels) do not show up in a proper sequence on the X-axis of the plotted graph. For example, instead of all the 2018 dates (e.g. 2018/02/15, 2018/03/10, 2018/10/12 ... 2019/01/07, 2019/01/10, 2019/03/16 ...), I would have these dates showing up on the X-axis in a mismatch order. For example 2019/01/07, 2019/01/10, 2018/02/15, 2018/03/10, 2019/03/16 ... even though I have applied sorting to the indexes (i.e. the dates). How do I handle this issue? Thank you in advance.
I tried to sort the indexes but this did not work.
DTT_data = miniBid_data.groupby(['Mini_Bid_Date_2'])['New_Cost_Per_Load','Volume'].aggregate([np.mean])
# sort the data
DTT_data.sort_index(inplace=True, ascending=True)
fig, ax = plt.subplots()
color1 = 'tab:red'
DTT_data.plot(kind='line', figsize=(12,8), legend=False, ax=ax, logy=True, marker='*')
ax.set_title('Trends of Selected Variables')
ax.set_ylabel('Log10 of Values', color=color1)
ax.legend(loc='upper left')
ax.set_xlabel('Event Dates')
ax.tick_params(axis='y', labelcolor=color1)
#ax.legend(loc='upper left')
ax1 = ax.twinx()
color2 = 'tab:blue'
DTT_data2 = miniBid_data.groupby(['Mini_Bid_Date_2'])['Carrier_Code'].count()
DTT_data2.plot(kind='bar', figsize=(12,8), legend=False, ax=ax1, color=color2)
DTT_data2.sort_index(inplace=True, ascending=False)
ax1.set_ylabel('Log10 of Values', color=color2)
ax1.set_yscale('log')
ax1.tick_params(axis='y', labelcolor=color2)
ax1.legend(loc='upper right')
fig.autofmt_xdate()
fig.tight_layout()
plt.show()
Sample Data:
a) DTT_data =
Mini_Bid_Date_2 New_Cost_Per_Load Volume
01/07/2019 1604.3570393487105 1.6431478968792401
02/25/2018 1816.1534797297306 2.831081081081081
10/22/2018 1865.5403827160494 2.074074074074074
10/29/2018 1945.3011032028478 1.9023576512455516
01/08/2019 1947.7562972972971 1.162162162162162
02/11/2019 2062.7133737931017 2.3424827586206916
11/05/2018 2095.531836956521 1.7753623188405796
12/08/2018 2155.48935907859 1.437825203252031
02/04/2019 2169.209245791246 2.2669696969696966
02/04/2018 2189.3693333333335 5.0
01/14/2019 2313.3854211711728 1.1587162162162181
01/20/2019 2380.9063928571427 1.0
01/21/2019 2631.0407864661634 1.3657894736842129
12/03/2018 2684.0808513089005 4.402827225130894
02/25/2019 2844.047048492792 1.89397116644823
11/12/2018 3011.510282722513 2.147905759162304
10/08/2018 3042.3035776536312 1.8130726256983247
11/19/2018 3063.736631460676 1.7407865168539327
02/18/2019 3148.531689480355 6.798162230671736
10/01/2018 3248.0486851851842 2.1951388888888905
01/19/2019 3291.1334154589376 1.4626086956521749
10/15/2018 11881.90527833753 1.779911838790932
01/28/2019 13786.149445804196 1.6329195804195813
03/04/2019 14313.741501103752 1.5459455481972018
12/10/2018 100686.89588865546 3.051260504201676
b) DTT_data =
Mini_Bid_Date_2 Carrier_Code
12/08/2018 1476
03/04/2019 1359
02/04/2019 1188
10/29/2018 1124
12/03/2018 955
10/08/2018 895
11/19/2018 890
10/15/2018 794
02/18/2019 789
02/25/2019 763
01/07/2019 737
02/11/2019 725
01/21/2019 665
10/01/2018 648
02/25/2018 592
01/28/2019 572
12/10/2018 476
01/14/2019 444
11/12/2018 382
10/22/2018 324
11/05/2018 276
01/19/2019 207
01/20/2019 56
01/08/2019 37
02/04/2018 30
My expectation is to have dates (indexes) in this case show up in sequential order, for example, 2019/01/07, 2019/01/10, 2018/02/15, 2018/03/10, 2019/03/16 ... on as labels on the X-axis.
pandas
asked Mar 25 at 5:59
Vondoe79Vondoe79
859
add a comment |
I have a pandas dataframe with dates as the indexes (indices). When I plot the values, the indexes(dates, i.e. the X-axis labels) do not show up in a proper sequence on the X-axis of the plotted graph. For example, instead of all the 2018 dates (e.g. 2018/02/15, 2018/03/10, 2018/10/12 ... 2019/01/07, 2019/01/10, 2019/03/16 ...), I would have these dates showing up on the X-axis in a mismatch order. For example 2019/01/07, 2019/01/10, 2018/02/15, 2018/03/10, 2019/03/16 ... even though I have applied sorting to the indexes (i.e. the dates). How do I handle this issue? Thank you in advance.
I tried to sort the indexes but this did not work.
DTT_data = miniBid_data.groupby(['Mini_Bid_Date_2'])['New_Cost_Per_Load','Volume'].aggregate([np.mean])
# sort the data
DTT_data.sort_index(inplace=True, ascending=True)
fig, ax = plt.subplots()
color1 = 'tab:red'
DTT_data.plot(kind='line', figsize=(12,8), legend=False, ax=ax, logy=True, marker='*')
ax.set_title('Trends of Selected Variables')
ax.set_ylabel('Log10 of Values', color=color1)
ax.legend(loc='upper left')
ax.set_xlabel('Event Dates')
ax.tick_params(axis='y', labelcolor=color1)
#ax.legend(loc='upper left')
ax1 = ax.twinx()
color2 = 'tab:blue'
DTT_data2 = miniBid_data.groupby(['Mini_Bid_Date_2'])['Carrier_Code'].count()
DTT_data2.plot(kind='bar', figsize=(12,8), legend=False, ax=ax1, color=color2)
DTT_data2.sort_index(inplace=True, ascending=False)
ax1.set_ylabel('Log10 of Values', color=color2)
ax1.set_yscale('log')
ax1.tick_params(axis='y', labelcolor=color2)
ax1.legend(loc='upper right')
fig.autofmt_xdate()
fig.tight_layout()
plt.show()
Sample Data:
a) DTT_data =
Mini_Bid_Date_2 New_Cost_Per_Load Volume
01/07/2019 1604.3570393487105 1.6431478968792401
02/25/2018 1816.1534797297306 2.831081081081081
10/22/2018 1865.5403827160494 2.074074074074074
10/29/2018 1945.3011032028478 1.9023576512455516
01/08/2019 1947.7562972972971 1.162162162162162
02/11/2019 2062.7133737931017 2.3424827586206916
11/05/2018 2095.531836956521 1.7753623188405796
12/08/2018 2155.48935907859 1.437825203252031
02/04/2019 2169.209245791246 2.2669696969696966
02/04/2018 2189.3693333333335 5.0
01/14/2019 2313.3854211711728 1.1587162162162181
01/20/2019 2380.9063928571427 1.0
01/21/2019 2631.0407864661634 1.3657894736842129
12/03/2018 2684.0808513089005 4.402827225130894
02/25/2019 2844.047048492792 1.89397116644823
11/12/2018 3011.510282722513 2.147905759162304
10/08/2018 3042.3035776536312 1.8130726256983247
11/19/2018 3063.736631460676 1.7407865168539327
02/18/2019 3148.531689480355 6.798162230671736
10/01/2018 3248.0486851851842 2.1951388888888905
01/19/2019 3291.1334154589376 1.4626086956521749
10/15/2018 11881.90527833753 1.779911838790932
01/28/2019 13786.149445804196 1.6329195804195813
03/04/2019 14313.741501103752 1.5459455481972018
12/10/2018 100686.89588865546 3.051260504201676
b) DTT_data =
Mini_Bid_Date_2 Carrier_Code
12/08/2018 1476
03/04/2019 1359
02/04/2019 1188
10/29/2018 1124
12/03/2018 955
10/08/2018 895
11/19/2018 890
10/15/2018 794
02/18/2019 789
02/25/2019 763
01/07/2019 737
02/11/2019 725
01/21/2019 665
10/01/2018 648
02/25/2018 592
01/28/2019 572
12/10/2018 476
01/14/2019 444
11/12/2018 382
10/22/2018 324
11/05/2018 276
01/19/2019 207
01/20/2019 56
01/08/2019 37
02/04/2018 30
My expectation is to have dates (indexes) in this case show up in sequential order, for example, 2019/01/07, 2019/01/10, 2018/02/15, 2018/03/10, 2019/03/16 ... on as labels on the X-axis.
pandas
asked Mar 25 at 5:59
Vondoe79Vondoe79
859
Did you convert to date column to datetime format and then sorted it? or if the column is in str then it sorts only lexicographically
– Justice_Lords
Mar 25 at 6:39
Yes, I did. This is how I did it - data['Mini_Bid_Date_2'] = pd.to_datetime(data['Mini_Bid_Date_2'], format='%m/%d/%Y')
– Vondoe79
Mar 25 at 13:54
add a comment |
I have a pandas dataframe with dates as the indexes (indices). When I plot the values, the indexes(dates, i.e. the X-axis labels) do not show up in a proper sequence on the X-axis of the plotted graph. For example, instead of all the 2018 dates (e.g. 2018/02/15, 2018/03/10, 2018/10/12 ... 2019/01/07, 2019/01/10, 2019/03/16 ...), I would have these dates showing up on the X-axis in a mismatch order. For example 2019/01/07, 2019/01/10, 2018/02/15, 2018/03/10, 2019/03/16 ... even though I have applied sorting to the indexes (i.e. the dates). How do I handle this issue? Thank you in advance.
I tried to sort the indexes but this did not work.
DTT_data = miniBid_data.groupby(['Mini_Bid_Date_2'])['New_Cost_Per_Load','Volume'].aggregate([np.mean])
# sort the data
DTT_data.sort_index(inplace=True, ascending=True)
fig, ax = plt.subplots()
color1 = 'tab:red'
DTT_data.plot(kind='line', figsize=(12,8), legend=False, ax=ax, logy=True, marker='*')
ax.set_title('Trends of Selected Variables')
ax.set_ylabel('Log10 of Values', color=color1)
ax.legend(loc='upper left')
ax.set_xlabel('Event Dates')
ax.tick_params(axis='y', labelcolor=color1)
#ax.legend(loc='upper left')
ax1 = ax.twinx()
color2 = 'tab:blue'
DTT_data2 = miniBid_data.groupby(['Mini_Bid_Date_2'])['Carrier_Code'].count()
DTT_data2.plot(kind='bar', figsize=(12,8), legend=False, ax=ax1, color=color2)
DTT_data2.sort_index(inplace=True, ascending=False)
ax1.set_ylabel('Log10 of Values', color=color2)
ax1.set_yscale('log')
ax1.tick_params(axis='y', labelcolor=color2)
ax1.legend(loc='upper right')
fig.autofmt_xdate()
fig.tight_layout()
plt.show()
Sample Data:
a) DTT_data =
Mini_Bid_Date_2 New_Cost_Per_Load Volume
01/07/2019 1604.3570393487105 1.6431478968792401
02/25/2018 1816.1534797297306 2.831081081081081
10/22/2018 1865.5403827160494 2.074074074074074
10/29/2018 1945.3011032028478 1.9023576512455516
01/08/2019 1947.7562972972971 1.162162162162162
02/11/2019 2062.7133737931017 2.3424827586206916
11/05/2018 2095.531836956521 1.7753623188405796
12/08/2018 2155.48935907859 1.437825203252031
02/04/2019 2169.209245791246 2.2669696969696966
02/04/2018 2189.3693333333335 5.0
01/14/2019 2313.3854211711728 1.1587162162162181
01/20/2019 2380.9063928571427 1.0
01/21/2019 2631.0407864661634 1.3657894736842129
12/03/2018 2684.0808513089005 4.402827225130894
02/25/2019 2844.047048492792 1.89397116644823
11/12/2018 3011.510282722513 2.147905759162304
10/08/2018 3042.3035776536312 1.8130726256983247
11/19/2018 3063.736631460676 1.7407865168539327
02/18/2019 3148.531689480355 6.798162230671736
10/01/2018 3248.0486851851842 2.1951388888888905
01/19/2019 3291.1334154589376 1.4626086956521749
10/15/2018 11881.90527833753 1.779911838790932
01/28/2019 13786.149445804196 1.6329195804195813
03/04/2019 14313.741501103752 1.5459455481972018
12/10/2018 100686.89588865546 3.051260504201676
b) DTT_data =
Mini_Bid_Date_2 Carrier_Code
12/08/2018 1476
03/04/2019 1359
02/04/2019 1188
10/29/2018 1124
12/03/2018 955
10/08/2018 895
11/19/2018 890
10/15/2018 794
02/18/2019 789
02/25/2019 763
01/07/2019 737
02/11/2019 725
01/21/2019 665
10/01/2018 648
02/25/2018 592
01/28/2019 572
12/10/2018 476
01/14/2019 444
11/12/2018 382
10/22/2018 324
11/05/2018 276
01/19/2019 207
01/20/2019 56
01/08/2019 37
02/04/2018 30
My expectation is to have dates (indexes) in this case show up in sequential order, for example, 2019/01/07, 2019/01/10, 2018/02/15, 2018/03/10, 2019/03/16 ... on as labels on the X-axis.
pandas
asked Mar 25 at 5:59
Vondoe79Vondoe79
859
I have a pandas dataframe with dates as the indexes (indices). When I plot the values, the indexes(dates, i.e. the X-axis labels) do not show up in a proper sequence on the X-axis of the plotted graph. For example, instead of all the 2018 dates (e.g. 2018/02/15, 2018/03/10, 2018/10/12 ... 2019/01/07, 2019/01/10, 2019/03/16 ...), I would have these dates showing up on the X-axis in a mismatch order. For example 2019/01/07, 2019/01/10, 2018/02/15, 2018/03/10, 2019/03/16 ... even though I have applied sorting to the indexes (i.e. the dates). How do I handle this issue? Thank you in advance.
I tried to sort the indexes but this did not work.
DTT_data = miniBid_data.groupby(['Mini_Bid_Date_2'])['New_Cost_Per_Load','Volume'].aggregate([np.mean])
# sort the data
DTT_data.sort_index(inplace=True, ascending=True)
fig, ax = plt.subplots()
color1 = 'tab:red'
DTT_data.plot(kind='line', figsize=(12,8), legend=False, ax=ax, logy=True, marker='*')
ax.set_title('Trends of Selected Variables')
ax.set_ylabel('Log10 of Values', color=color1)
ax.legend(loc='upper left')
ax.set_xlabel('Event Dates')
ax.tick_params(axis='y', labelcolor=color1)
#ax.legend(loc='upper left')
ax1 = ax.twinx()
color2 = 'tab:blue'
DTT_data2 = miniBid_data.groupby(['Mini_Bid_Date_2'])['Carrier_Code'].count()
DTT_data2.plot(kind='bar', figsize=(12,8), legend=False, ax=ax1, color=color2)
DTT_data2.sort_index(inplace=True, ascending=False)
ax1.set_ylabel('Log10 of Values', color=color2)
ax1.set_yscale('log')
ax1.tick_params(axis='y', labelcolor=color2)
ax1.legend(loc='upper right')
fig.autofmt_xdate()
fig.tight_layout()
plt.show()
Sample Data:
a) DTT_data =
Mini_Bid_Date_2 New_Cost_Per_Load Volume
01/07/2019 1604.3570393487105 1.6431478968792401
02/25/2018 1816.1534797297306 2.831081081081081
10/22/2018 1865.5403827160494 2.074074074074074
10/29/2018 1945.3011032028478 1.9023576512455516
01/08/2019 1947.7562972972971 1.162162162162162
02/11/2019 2062.7133737931017 2.3424827586206916
11/05/2018 2095.531836956521 1.7753623188405796
12/08/2018 2155.48935907859 1.437825203252031
02/04/2019 2169.209245791246 2.2669696969696966
02/04/2018 2189.3693333333335 5.0
01/14/2019 2313.3854211711728 1.1587162162162181
01/20/2019 2380.9063928571427 1.0
01/21/2019 2631.0407864661634 1.3657894736842129
12/03/2018 2684.0808513089005 4.402827225130894
02/25/2019 2844.047048492792 1.89397116644823
11/12/2018 3011.510282722513 2.147905759162304
10/08/2018 3042.3035776536312 1.8130726256983247
11/19/2018 3063.736631460676 1.7407865168539327
02/18/2019 3148.531689480355 6.798162230671736
10/01/2018 3248.0486851851842 2.1951388888888905
01/19/2019 3291.1334154589376 1.4626086956521749
10/15/2018 11881.90527833753 1.779911838790932
01/28/2019 13786.149445804196 1.6329195804195813
03/04/2019 14313.741501103752 1.5459455481972018
12/10/2018 100686.89588865546 3.051260504201676
b) DTT_data =
Mini_Bid_Date_2 Carrier_Code
12/08/2018 1476
03/04/2019 1359
02/04/2019 1188
10/29/2018 1124
12/03/2018 955
10/08/2018 895
11/19/2018 890
10/15/2018 794
02/18/2019 789
02/25/2019 763
01/07/2019 737
02/11/2019 725
01/21/2019 665
10/01/2018 648
02/25/2018 592
01/28/2019 572
12/10/2018 476
01/14/2019 444
11/12/2018 382
10/22/2018 324
11/05/2018 276
01/19/2019 207
01/20/2019 56
01/08/2019 37
02/04/2018 30
My expectation is to have dates (indexes) in this case show up in sequential order, for example, 2019/01/07, 2019/01/10, 2018/02/15, 2018/03/10, 2019/03/16 ... on as labels on the X-axis.
pandas
pandas
asked Mar 25 at 5:59
Vondoe79Vondoe79
859
asked Mar 25 at 5:59
Vondoe79Vondoe79
859
asked Mar 25 at 5:59
Vondoe79Vondoe79
859
asked Mar 25 at 5:59
Vondoe79Vondoe79
859
asked Mar 25 at 5:59
Vondoe79Vondoe79
859
859
Did you convert to date column to datetime format and then sorted it? or if the column is in str then it sorts only lexicographically
– Justice_Lords
Mar 25 at 6:39
Yes, I did. This is how I did it - data['Mini_Bid_Date_2'] = pd.to_datetime(data['Mini_Bid_Date_2'], format='%m/%d/%Y')
– Vondoe79
Mar 25 at 13:54
add a comment |
Did you convert to date column to datetime format and then sorted it? or if the column is in str then it sorts only lexicographically
– Justice_Lords
Mar 25 at 6:39
Yes, I did. This is how I did it - data['Mini_Bid_Date_2'] = pd.to_datetime(data['Mini_Bid_Date_2'], format='%m/%d/%Y')
– Vondoe79
Mar 25 at 13:54
Did you convert to date column to datetime format and then sorted it? or if the column is in str then it sorts only lexicographically
– Justice_Lords
Mar 25 at 6:39
Did you convert to date column to datetime format and then sorted it? or if the column is in str then it sorts only lexicographically
– Justice_Lords
Mar 25 at 6:39
Yes, I did. This is how I did it - data['Mini_Bid_Date_2'] = pd.to_datetime(data['Mini_Bid_Date_2'], format='%m/%d/%Y')
– Vondoe79
Mar 25 at 13:54
Yes, I did. This is how I did it - data['Mini_Bid_Date_2'] = pd.to_datetime(data['Mini_Bid_Date_2'], format='%m/%d/%Y')
– Vondoe79
Mar 25 at 13:54
add a comment |
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Did you convert to date column to datetime format and then sorted it? or if the column is in str then it sorts only lexicographically
– Justice_Lords
Mar 25 at 6:39
Yes, I did. This is how I did it - data['Mini_Bid_Date_2'] = pd.to_datetime(data['Mini_Bid_Date_2'], format='%m/%d/%Y')
– Vondoe79
Mar 25 at 13:54