Add link to .csv-file to import
Tim Skov Jacobsen
parent
d1c15f682a
commit
76848cee3d
|
|
@ -14,7 +14,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -31,7 +31,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -90,7 +90,7 @@
|
|||
"2 13 23 33"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -115,7 +115,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -144,7 +144,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -203,7 +203,7 @@
|
|||
"2 120 314 0.382166"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -236,7 +236,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -302,7 +302,7 @@
|
|||
"3 120 314 0.382166"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -327,7 +327,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -383,7 +383,7 @@
|
|||
"3 0.382166"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -402,7 +402,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -415,7 +415,7 @@
|
|||
"Name: sigma_n [GPa], dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -436,7 +436,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -497,7 +497,7 @@
|
|||
"3 120 0.382166"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -556,7 +556,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -707,7 +707,7 @@
|
|||
"10 HE300A 290 300 182600000 1260.0 88.3"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -730,7 +730,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -831,7 +831,7 @@
|
|||
"10 HE300A 290 300 182600000 1260.0 88.3"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -857,7 +857,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -877,7 +877,7 @@
|
|||
"Name: Iy[mm4], dtype: bool"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -901,7 +901,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -982,7 +982,7 @@
|
|||
"8 HE260A 250 260 104500000 836.0 68.2"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1007,7 +1007,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -1078,7 +1078,7 @@
|
|||
"9 HE280A 270 280 136700000 1010.0 76.4"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1102,7 +1102,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -1173,7 +1173,7 @@
|
|||
"10 HE300A 290 300 182600000 1260.0 88.3"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1214,7 +1214,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -1299,7 +1299,7 @@
|
|||
"7 Pear 6"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1321,16 +1321,16 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001C6E0EC2388>"
|
||||
"<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001EC320855C8>"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1351,7 +1351,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -1412,7 +1412,7 @@
|
|||
"Pear 9"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1583,16 +1583,18 @@
|
|||
"***All exercises 2.x are to be seen as the same problem. It has just been divided into smaller tasks.***\n",
|
||||
"\n",
|
||||
"---\n",
|
||||
"Import the file `shear_key_forces.csv` to a DataFrame using `pandas.read_csv()`. The values in the file are comma separated, which the function also assumes as default. \n",
|
||||
"The file is located in the Session 5 folder and has 104329 rows. Print the head or the tail to see the imported data.\n",
|
||||
"This exercise works with a file called `shear_key_forces.csv`. To get the file go [here](https://raw.githubusercontent.com/Python-Crash-Course/Python101/master/Session%205%20-%20Dataframes/shear_key_forces.csv), right-click and choose \"save as\". Select `.csv` as file format. Save the file in the same folder as your script for this exercise. \n",
|
||||
"\n",
|
||||
"Inside your script, import the file `shear_key_forces.csv` to a DataFrame using `pandas.read_csv()`. The values in the file are comma separated, which the function also assumes as default. \n",
|
||||
"The file has 104329 rows. Print the head or the tail to see the imported data.\n",
|
||||
"\n",
|
||||
"*The data has all spring element forces in a bunch of load cases from a Sofistik finite element calculation. It's not strictly necessary to know what the data represents. It could just be looked at as dummy data to work with.*\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Exercise 2.2\n",
|
||||
"*The model has many spring elements. Some of them represent shear keys between the structural parts of a tunnel at movement joint locations. These are the one we are going to extract.*\n",
|
||||
"*The model has many spring elements. Some of them represent shear keys between the structural parts of a tunnel at movement joint locations. These are the ones we are going to extract.*\n",
|
||||
"\n",
|
||||
"The data has a column `'shear_key'` which has the name of the shear key if the element in that row is part of a shear key. E.g. `'Shear_key1'`. If the element is not part of a shear key, the name is `'Not_a_shear_key'`.\n",
|
||||
"The data has a column `'shear_key'` which has the name of the shear key if the element in that row is part of a shear key in the model. E.g. `'Shear_key1'`. If the element is not part of a shear key, the name is `'Not_a_shear_key'`.\n",
|
||||
"\n",
|
||||
"Filter out all rows which are not part of a shear key. The resulting DataFrame should have 2874 rows. \n",
|
||||
"\n",
|
||||
|
|
@ -1606,13 +1608,13 @@
|
|||
"df = df.drop('column_name', axis=1)\n",
|
||||
"~~~\n",
|
||||
"---\n",
|
||||
"The argument `axis=1` specifies that it is a column and not a row that should be removed.\n",
|
||||
"The argument `axis=1` specifies that it is a column that should be removed, whereas `axis=0` would represent removal of a row.\n",
|
||||
"\n",
|
||||
"*Remember to save to a new variable or use argument `inplace=True`. If you save to a variable, you can use the same name to 'overwrite' the old one if it's not needed anymore.*\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Exercise 2.4\n",
|
||||
"*Each shear key consists of three spring elements. The total force that the shear key should be designed for is the sum of those three spring forces.*\n",
|
||||
"*Each shear key consists of three spring elements. The total force in the shear key is the sum of those three spring forces.*\n",
|
||||
"\n",
|
||||
"Create a DataFrame with the sum of the three values within each shear key for every load case. The resulting DataFrame should have 958 rows.\n",
|
||||
"\n",
|
||||
|
|
@ -1627,7 +1629,7 @@
|
|||
"Here, a list of column labels is passed in the `groupby()` method instead of just a single column label. The first column `'Shear_key'` is what is used to create the groups, while consecutive labels just follow. Any columns that are not passed in will not appear in the resulting DataFrame. \n",
|
||||
"\n",
|
||||
"# Exercise 2.5\n",
|
||||
"Filter the DataFrame for a shear key, for example `'Shear_key1'` and create a bar plot of it with the `DataFrame.plot()` method. The bar plot should have the load cases as $x$-values and the force $P$ [kN] as $y$-values.\n",
|
||||
"Filter the DataFrame for a specific shear key, for example `'Shear_key1'` and create a bar plot of it with the `DataFrame.plot()` method. The bar plot should have the load cases as $x$-values and the force $P$ [kN] as $y$-values.\n",
|
||||
"\n",
|
||||
"---\n",
|
||||
"~~~python\n",
|
||||
|
|
@ -1655,7 +1657,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -1928,7 +1930,7 @@
|
|||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue