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--- add_dates [2020/12/03 18:58] – [offset calculation] raju
+++ add_dates [2020/12/03 20:15] – [single start date; multiple offsets; with pandas] raju
@@ Line 1: / Line 1: @@
 === Add dates ===
-==== with pandas; multiple dates ====
+==== single start date; multiple offsets; with pandas ====
+prepare the input
+<code>
+In [1]:
+start_yyyymmdd = '20201203'
+fmt = '%Y%m%d'
+from datetime import datetime
+start_date = datetime.strptime(start_yyyymmdd, fmt)
+In [2]:
+print(start_date)
+-12-03 00:00:00
+In [3]:
+import pandas as pd
+days_original = [158, 928, 882, 341, 596, 878, 526]
+offset_years = [round(x/365.0, 9) for x in days_original]
+df = pd.DataFrame({'offset_years': offset_years})
+In [4]:
+print(df)
+   offset_years
+      0.432877
+      2.542466
+      2.416438
+      0.934247
+      1.632877
+      2.405479
+      1.441096
+</code>
+Convert the offset to days
+<code>
+In [5]:
+import numpy as np
+df['offset_days'] = (df['offset_years']*365.0).round().astype(np.int)
+df
+Out[5]:
+   offset_years  offset_days
+      0.432877          158
+      2.542466          928
+      2.416438          882
+      0.934247          341
+      1.632877          596
+      2.405479          878
+      1.441096          526
+</code>
+Apply the offset to get end dates
+<code>
+In [6]:
+df['end_yyyymmdd'] = (start_date + pd.to_timedelta(df['offset_days'], 'd')).dt.strftime(fmt)
+df
+Out[6]:
+   offset_years  offset_days end_yyyymmdd
+      0.432877          158     20210510
+      2.542466          928     20230619
+      2.416438          882     20230504
+      0.934247          341     20211109
+      1.632877          596     20220722
+      2.405479          878     20230430
+      1.441096          526     20220513
+</code>
+Used | Python 3.8.5, ipython 7.18.1, pandas 1.1.3, numpy 1.19.2
+demonstrates | round float to int
+==== multiple dates; different offsets; with pandas ====
 Given
 <code>
@@ Line 67: / Line 134: @@
 </code>
-==== without pandas; single date ====
+==== single date; single offset; without pandas ====
-For a single date, we can do this without pandas also
 <code>
 In [1]:
@@ Line 82: / Line 148: @@
 ==== Related links ====
   * https://github.com/KamarajuKusumanchi/market_data_processor/blob/master/add_days.py
+  * https://pandas.pydata.org/docs/reference/api/pandas.to_timedelta.html
 ==== offset calculation ====
@@ Line 87: / Line 154: @@
 For example, let's say:
-  * There are two applications App1 and App2 where App1 provides input to App2
+  * There are two applications App1 and App2
-  * App1 converts the number of days (an integer value) into number of years (a floating point number) and rounds it to 9 digits and gives it to App2.
+  * App1 converts the number of days (an integer value) into number of years (a floating point number) and rounds it to 9 digits
+  * App2 reads that as input
 <code>
@@ Line 114: / Line 182: @@
 </code>
-==== single start date; multiple offsets; with pandas ====
-<code>
-In [1]:
-start_yyyymmdd = '20201203'
-fmt = '%Y%m%d'
-from datetime import datetime
-start_date = datetime.strptime(start_yyyymmdd, fmt)
-In [2]:
-print(start_date)
--12-03 00:00:00
-In [3]:
-import pandas as pd
-days_original = [158, 928, 882, 341, 596, 878, 526]
-offset_years = [round(x/365.0, 9) for x in days_original]
-df = pd.DataFrame({'offset_years': offset_years})
-In [4]:
-print(df)
-   offset_years
-      0.432877
-      2.542466
-      2.416438
-      0.934247
-      1.632877
-      2.405479
-      1.441096
-In [5]:
-import numpy as np
-df['offset_days'] = (df['offset_years']*365.0).round().astype(np.int)
-df
-Out[5]:
-   offset_years  offset_days
-      0.432877          158
-      2.542466          928
-      2.416438          882
-      0.934247          341
-      1.632877          596
-      2.405479          878
-      1.441096          526
-In [6]:
-df['end_yyyymmdd'] = (start_date + pd.to_timedelta(df['offset_days'], 'd')).dt.strftime(fmt)
-df
-Out[6]:
-   offset_years  offset_days end_yyyymmdd
-      0.432877          158     20210510
-      2.542466          928     20230619
-      2.416438          882     20230504
-      0.934247          341     20211109
-      1.632877          596     20220722
-      2.405479          878     20230430
-      1.441096          526     20220513
-</code>