1

app.py

@@ -1,3 +1,4 @@
+import os, re, sys, time, math, shutil, urllib, string, random, pickle, zipfile, datetime
 import streamlit as st, pandas as pd, numpy as np
 
 st.set_page_config(page_title="Предсказание V", page_icon="🦋", layout="wide") # set_page_config() can only be called once per app page, and must be called as the first Streamlit command in your script.
@@ -8,11 +9,15 @@ st.html(my_stm.STYLE_CORRECTION)
 st.sidebar.markdown("💎 Стартовая страница")
 
 df = pd.DataFrame([
-        {"command": "st.selectbox", "rating": 4, "is_widget": True},
-        {"command": "st.balloons", "rating": 5, "is_widget": False},
-        {"command": "st.time_input", "rating": 3, "is_widget": True},
+{"command": "st.selectbox", "rating": 4, "is_widget": True},
+{"command": "st.balloons", "rating": 5, "is_widget": False},
+{"command": "st.time_input", "rating": 3, "is_widget": True},
 ])
 
 edited_df = st.sidebar.data_editor(df, num_rows="dynamic")
 favorite_command = edited_df.loc[edited_df["rating"].idxmax()]["command"]
-st.sidebar.markdown(f"Your favorite command is **{favorite_command}** 🎚️")
+st.sidebar.markdown(f"Your favorite command is **{favorite_command}** 🎚️")
+
+
+with st.container():
+    st.write(os.getcwd())

pages/9_Таблица_результатов.py

@@ -3,4 +3,5 @@ import my_static_methods as my_stm
 st.markdown(my_stm.STYLE_CORRECTION, unsafe_allow_html=True)
 
 st.sidebar.markdown("###  просто таблица случайных чисел - пока заглушка ❄️")
-st.dataframe(my_stm.df_random_dataframe())
+pop = st.sidebar.popover("Open popover")
+pop.dataframe(my_stm.df_random_dataframe())

static/test.ipynb

@@ -454,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -500,7 +500,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -512,35 +512,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df2.groupby(\"ID\").apply(lambda df: df.tail(1))\n",
+    "#df2.groupby(\"ID\").apply(lambda df: type(df))\n",
+    "#set(df2.groupby(\"ID\").apply(lambda df: len(df)).values)\n",
+    "\n",
+    "#df2.groupby(\"ID\").apply(lambda df: list(df.columns))\n",
+    "#df2.groupby(\"ID\").apply(lambda df: list(df.columns))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\f64\\AppData\\Local\\Temp\\ipykernel_6328\\3316428820.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
-      "  set(df2.groupby(\"ID\").apply(len))\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "{7, 14}"
+       "'n:\\\\Makarov\\\\Development\\\\HuggingFaceSpacesGit\\\\streamlit\\\\static'"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#df2.groupby(\"ID\").apply(lambda df: df.tail(1))\n",
-    "#df2.groupby(\"ID\").apply(lambda df: type(df))\n",
-    "#set(df2.groupby(\"ID\").apply(lambda df: len(df)).values)\n",
-    "\n",
-    "#df2.groupby(\"ID\").apply(lambda df: list(df.columns))\n",
-    "#df2.groupby(\"ID\").apply(lambda df: list(df.columns))"
+    "os.getcwd()"
    ]
   }
  ],